Hello, and thank you for keeping us company. This is why in the morning, why two by four is the station. My name is Adereva Hilawe, a very good Monday morning to you. We are talking about the food security I'm joined by Beatrice Kairush, she's a health economist and Jogmugira is a research economist. Welcome so much as we talk about this. Let's get to know where we are as a nation, where we are headed to. Last week we had the Cabinet Secretary of Treasury Ambassador Kuriatan say the agricultural forestry and fishing sector grew by 3.6% in 2019 from 6.0% in 2018, which is a deceleration and of course it's attributed by the extreme weather phenomenon we experienced last year. You remember almost the first half of the year was graced by long drought where people died because of hunger and later we experienced floods like we are right now and 2020 is no different from 2019. We have a calamity that we are fighting right now. We have the floods that already has rendered thousands of people homeless. Food crops in our shambas have been destroyed by the rains and remember the first months of 2020 we experienced the invasion of desert locusts. Therefore we are really affected so much in this and of course we are still fighting the COVID-19 pandemic, the economy of our country is in a bad state but we want to see where we are as a nation and to speak of which we will be looking at a few areas the taxes on the farmers and this farm produce are we in a position as a country to produce enough for our people and even maybe to support other nations and what can be done to support our subsidence farmers and why should we keep on importing foods that we are growing here in our country send us your comments or reactions to all our social media platforms at Y254 channel. My handle is at Murani Hilovi. Welcome to the broadcast. Good morning ladies and gentlemen. Good morning. Katiboni sana. Now we are around the 50th of COVID-19. Let me begin with you Beatrice. How have you been coping with the situation? I think for me it's still I will not say usual because there are so many amenities that services that have gone down so some of us we love hanging out in joints restaurants now we cannot hang out anymore you have to stick in your house and for basically people who are extroverts it's becoming quite difficult because maybe we love sightseeing traveling from one point to another now people are being forced to confine but also in terms of healthcare and other aspects such as businesses they're quite being affected in one way or another people losing jobs it's it's stressful and even people now actually having to feed themselves have you know 50 days and you're a daily wage earner and you're not earning that daily wage is really affecting people in households just like recently the way we saw a lady in Kisauni we had a lady in Kisauni who was boiling stones for her children because her daily wage has already been been cut short so obviously such an individual already is at risk even when you're saying we are preventing covid but now such a mother even food hunger in itself it's actually going to affect the immunity even in a way of their bodies fighting the covid itself so basically it's affecting many people in one way or another but I think the most affected persons are those who are daily daily wage earners those ones are the most most most hit hard and small scale businesses definitely all right now jobit it has been a week since I asked you the same question I don't know whether you have changed the tactics or things are still no more for you of course Hilary because of hope I've been changing the tactics I'm hopeful that we will overcome but there over 50 days have not been is what disadvantage of being a researcher there's something we call hypothesis you work forming them you find yourself in a situation you form a hypothesis so since covid began I've been forming so many hypotheses and one of it is in fact what we are discussing today about food security and I strongly feel if we were food secure as a nation by now you could have knocked out covid yeah we will discuss further how you could have done it if we were food secure exactly and speaking of food security it's one of the big four agenda and just even before we get to the food security the economy of our country has been greatly affected are we looming at a economic shock as a nation after and even during this covid-19 betris definitely we gonna we gonna be hit very hard because look at the policies that have been been imposed we are quarantining people who we are not even sure that they are actually having covid itself so if that's a person who is earning a salary or they are going reporting to work somewhere already that person they are you've put them on a sick roll there's there's that term we call a sick roll that person is already on a sick roll that means if that person is a breadwinner what happens it has already affected the economic aspect but further to that systems now have to readjust what you are doing where people are reporting to work now maybe we may shift to online that means some jobs will be termed redundant right so the moment those jobs are termed redundant where do those people go people without skills in terms of technology and digitalization what happens to such individuals they'll be forced to go back either to school or I don't know because at the end of the day they'll be they'll have to readjust people have to start readjusting because now I have seen people what is it called going going from the office not to the home so what will happen is that even the rent of these offices in towns what happens to the land roads people might convert them to to be studios for people to live in because so those offices will be left empty right look at the malls that were established with office spaces what happens people are taking the office furniture back home because now that we are going online and we are going to go digitalized definitely now we are going to reduce to people are people are reducing what is it called people are reducing the even the expenditure right now or in that the people are projecting that covid will extend maybe to september or the end of the year there was some data that was showing maybe for the whole world to say that they are covid free it might extend all the way to december and to some other news they are saying maybe even a year from now we are still going to be suspicious still about the covid right so what will happen is that many businesses will have to readjust and they'll have to cut uh the the employees right because you don't want to spend where you're not sure about the economic stability uh but on the other end there are people who are going to benefit from it right in every crisis they're always uh they're always benefactors right there's always something that's going to benefit right so businesses are cropping up like now the masks we've seen people creating cloth masks uh it may not be super effective uh in terms of preventing transmission of the infection but uh that mamamboga maybe shifted from selling bogas and now has shifted to to selling of masks which is still generating income so sanitizers and other products of uh for reduction of the spread of the infection when you think about it it has opened business avenues for other people actually one since we are talking about food security uh remember there is this concept of people boosting the immunity and lemons you've seen around social media people are talking about take your lemons take your oranges so the fruit business when you think about it a farmer who had lemons already in his shamba they've really benefited right they've really benefited and anybody selling oranges and those citrus kind of foods because now people have become health conscious now because people are also eating at home so they're not eating at restaurants so what will happen the the farmer most likely his job is not redundant because we all depend on food to boost our immunity in one way or another so i'm suspecting after this someone will go to muduru and get those foods to sell to people going back home but let me come to you with a different angle in this we have been greatly affected in terms of food security right from last year we had complaints even from cash crop farmers like the tea and coffee but now even the food the food uh stuff like the maize these people have been affected in terms of their pay they have produced maize uh there are claims the ncpb is not buying from them we have uh small scale farmers who who just tell their maize to anyone who will come along and uh we got to a point even of importing maize to support our people when we were hit by hunger this year we had the locust invasion at this point do you think as a nation we have policies to protect our farmers not at all hillary about policies we have them but now from a policy perspective what's the role of a policy or what are the stages of coming up with a policy once you write it and place it on the shelves it's just like a newspaper and i say man yakfunga nyama pay your policy neza tumuakfanya kazi zingini ata wasku li kukufunga nyama but once you fail to implement a policy in fact that's corruption in itself because it's spent resources coming up with a policy people went to naivasha to sit down and formulate the policies then you come and place it on the shelves the policies in the minister of agriculture and the CS knows in fact it was discussing these things not long ago from now and uh i will tell you whatever is in their website are like over 20 policies on various sectors of the agricultural sector but what is the implementation my mother dropped out of schooling from two some years back because the coffee sector that had helped her join high school started collapsing nowadays in my village in meru somewhere you cannot put your hope on kufi because the policies that once used to make kufi a booming agricultural sector now seem not to exist in a longer what happened is there any research that was done and said that kufi is not safe for human consumption so that now we reduce kufi production people are big but daily kazi akuhasol piya imezidi so i presume intake of kufi should even be going high because guys are not sleeping trying to hustle then these policies yeah and even it's cold but again now how comes that we import our kufi to to end up in meru to produce kahawa to let the mahalina irobi apa kura kura kidogo happen and these policies do not protect us we saw the T regulations i think two weeks ago and some people celebrated but they were celebrated because something in the tea sector is being done had they read those regulations they should go and read i will no one will read for them the tea farmer should go and read but about policies they are there but some of them are not well researched because in kenya and what is ailing us one thing we have to admit it is uh we cannot blame the sea us you know i never blame the government because i told you there are several times that you have met the government is my mother my father my sister my brother those people work in government it's our neighbors our fathers our mothers and all those so that's why i don't blame them the condemnation like uh i'm most likely people who come from the same same regions exactly now these are the guys who know who researched outside don't do it this way the way it's being done in kenya we do it the way it's being done maybe in another funny country then it will bring money towards without any care for the local farmer so our policies have failed to protect the farmers about us producing food we have enough land i don't know whether we used to say it's 584,000 square kilometers of kenyan land which which is very fertile god has blessed us but you are suffering from the so called natural resource cuts where you are bestowed with so many natural resources but the output from them is zero so it's not a matter of we are lacking anything we have everything but now people working in the minister of agriculture we i don't i don't want to blame them at this point let them just understand including the c s that at this point they have found the ministry the way it is but these are the cries of kenyans so this is the time we cannot continue doing things the way we've been doing or propagate what we found there let's start a new way of doing things as our friend has told us or she is suggesting and that's our hope at a ishe leho keshot ru di kuhasol now kii korona iki ishe leho to takua to mei pigana 57 days iki ru di next year at time like april to we get another case the world is declared corona free and then in 2021 god forbid march again march 12 we report another case will we take another 57 days to fight the same thing what will be our lessons will we will everybody fear a lockdown because they don't know what they will eat tomorrow and if we had food my friend korona imi ishe batsaisu kifungo otakula nini actually he has said something i wanted to ask you if we went on lockdown yes would we be in a position to feed our own people and if not then why are we as a nation not in a position to produce enough for our people and maybe to support others who may be dying of hunger is it our policies or is it that we have poor knowledge of farming what's our problem okay first we need to understand what is food security you know it's a terminology the president just gave one of my big four agendas is food security health universal health coverage housing for everybody but what is really food security when you talk about food security it means each and every citizen in that country can be able to access food not just any food because you can't eat ugali ugali ugali ugali every day so you need access of food that's one two nutritious food so it it is food yes but it should be nutritious and when it's nutritious what does it do it it boosts your health it boosts your health that means you live a healthy lifestyle because if you look at places like wajir moyale that were really hit catastrophically by drought and and other disasters and even in tukana you'll find malnutrition is very common in those in in those regions right because maybe they are only drinking porridge but they need their proteins they need their vitamins they need their vegetables so if this food if the citizens of that country cannot access a balanced diet that's what i would say they cannot access a balanced diet where they can access their proteins they can access their starch they can access their vitamins mangoes can reach someone in tukana oranges can reach someone in kisumu that are not uh producing oranges then we cannot say we have we are food secure okay so first we need to understand what is food security before we even create even policies of ensuring there is actual food security what what are you because when the government says they have reserves for maize but a human being cannot eat maize from january to december it it needs to say is when you have food reserves i can get an egg you know i can get meat right i can get skumawiki i can get popos that's what we are talking about right so until we first address that issue of food security first and and and government officials understanding what is what is it about uh when we say food security what are we talking about that that is one aspect number two so that in the case of a lockdown the government can the government be able to distribute food that is nutritious to eat in every household the 47 million kenyans in the country can eat now uh when we make policies it's at two levels there's the macro level and there's the micro level the macro level is on the national national level in the national level we are saying that when the government looks at its reserves it can be able to distribute food to all its 47 million kenya irrespective of your status whether you are rich or poor it can be able to do that countries like in the uk if they find they have food reserves that can go up to three years even if they are not producing any food they have reserves to the point if the worst of the worst happens they can actually feed their society for up to three years without any any any any food production now come back home right now even our national serial boards they're saying because you have to import more yeah they have to import when when when exactly but what has me most is for a mother in kisauni to boil stones to feed her children that already tells that already exposes us bare very very bare right that tells you uh how many other people that is only one case i will speak how many other cases are like that countrywide even in the rural areas there are people we may think the rural areas they have food many actresses are actually sleeping hungry before even we talk about lockdown in the cities all right so once we understand the macro macro macro level what policies and i'll agree with the with my friend job we make policies not from an informed point of view an informed point of view means you've done a research you have studied your people you have studied the weather patterns and end you sit down and come up with a policy that will cushion you so day in day out rukana is hit by a drought then we ask for a fund drive as if all those as if all those years we have never sat down and ask ourselves for how long can this continue right for how long can this continue so right now they would have already been policies of storing reserves to how many years food reserves how many years should we ensure that we have food reserves if it's the natural cereal board do we have maize that can feed a country for the next five years that's what we should be asking ourselves you know those are the kind of policies that we should in kenya let them even begin a policy for two months that when the rains are on setting in trukana yes whether manakopale the day announces that there is expected uh floods in trukana yes for two months yes so that now the floods will come and pass we feed those people for two months only or even one month yeah but uh do we do we have such a policy is it our farmers who are not growing maize or the food stuff you want or what happens do they farm the farmers are doing their work but uh you see now there are so many discouragements about continued being a farmer like in kenya and it's very sad and and we have to form a culture of resigning when serious complaints are brought and you're the person in charge of this thing there are two things the president should fire you or voluntarily you resign why do we have a maize deficit and again people people have seen with my eyes are drawing their maize to cows somewhere in uh was in gishu county and uh hila re people will talk about storage siguis itapata wevers and such in this you know the economy as i like saying it's always intertwined in this country there is a university i know that is offering a phd in food food i think food technology and post harvest studies now what does that university teach why do do those phd graduates which run into hundreds where do they go why can they be positioned by the government in the minister of agriculture to somewhere in the food baskets of kitale and uh eldoret that post harvest technology that they have been taught at phd level you remember in corona we had a phd in hand washing person in this country who came to show us how to wash hands let us get those people in post harvest technology when the eldoret when the kitale when the meru they set base there they show us how to preserve our food and then as you finish i'll discuss why we like importing you were mentioning about the information coming up with the policy on an informed basis yeah very uninformed basis but to touch on what you asked about what are the major challenges that affect the agricultural sector there are various things one is is low farm level production low farm level because we we have to accept kenya there is private ownership of land and most of the agricultural production that happens within the country is from small scale farmers so you have your kakota acre of piece of land and you're growing your onions another one has a two acre piece of land somewhere yeah but we also know there are those organizations that come together and they do large scale so wheat production maize production in large scale as part from also cash crop like tea and whatever but most of the food production that we actually um receive in our marikiti yeah wakulima market they are coming from small small farmers with small pieces of land they are fragmented pieces pieces of land but in terms of low level of production what kus is the low level of production one it's technology skills our farmer still depending on who and jembe and panga you know in this day January in this 21st century i was still using jembe and you know panga to still for mass production right and then the other thing is do we have credit support for a farmer i remember at some at some point i thought i could start affirming business right but then when you when you realize the cost that will it cost you to maybe even to produce things like onions or produce things like even carrots you need input in terms of money right so that you're able to buy the fertilizer you're able to buy uh to pay the casual laborers right so if i was a youth i walk into a bank can they believe in me and give me some credit support right and then the other news is is is lack of situational uh institutional support so various institutions so you are there you've produced your milk for example will your milk be bought you've worked very hard as a farmer you've farmed correct you've done everything correct but who buys your produce do you have institutional support or will it happen like in rifti where farmers were just pouring milk or will it happen like in baringo when farmers produce a lot of tomatoes then it all decayed in the farms because nobody is buying so farmers have really worked very hard even using their own simple technologies right but we don't have people who can even do value addition to such kind of produce for example instead of making the farmer poor milk can they be taught how to make taught how to make cheese and then we market our cheese and the farmer still makes money if it's the tomatoes can we can the tomatoes right so it reached to a point even now when you in Nairobi you are buying one piece of tomato one one one tomato for around 20 shillings right then the other thing is is point infrastructure so the farmer has made its produce but where are the roads yeah to remove the farm produce from the farm to reach the end consumer even if it's for export basis so already uh the the transport costs it what makes food prices even go high up so the way he's saying if a research is done to show how much transport costs affects the price of food to go up then the government can come in and maybe say let's reduce fuel prices that's how you make policies for any farmer who is farming farm produce can we reduce fuel prices for such individuals so that food can easily be accessible for each individual in the country uh then the thing is usually uh just as you said less access to credit so if you are a farmer can policies be put in place if you go to a bank do I still pay interest rates as anybody else right so because at the end of the day whether you are a king or you are a pauper we all depend on the farmer for food all of us experience hunger pangs irrespective of our social status so can policies be put in place to encourage people to actually adapt farming for example so if they reduce credit facilities for people who are engaging in farming business what do you expect people will go and farm but now when you imagine the cost that you're going to incur what what happens people will want and wait for a blue collar job and then last but not least is the high cost of the inputs if policies are put in place for example and subsidies government is able to give subsidies for example to the fertilizers to the tractors that can farm the land you know you reduce the the the input cost on such what happens the cost of production goes down and even the prices of food will go down such that even that person who is earning 100 chilings a day is able to afford a decent meal on top of that table when it's becoming very very expensive to eat healthy someone actually challenged me and said because you know eating healthy or in itself is expensive so it's even cheap to eat unhealthily ukule chips for 50 bob you're good to go but putting a decent balanced diet meal on your table becomes a very very expensive thing which is bad because what happens is when we eat the wrong diet what happens it starts affecting our health institutions already because you're overworked and you know healthcare in itself is not a revenue generating uh kind of business quote and quote right so when you start overwhelming your healthcare facilities because you are not creating policies that can allow production of nutritious food so that each and every individual each and every citizen can afford a balanced diet on the table then we are doing how will i will call it's like rubbing yourself you are putting here and also rubbing yourself on the other end at the end of the day yeah job she has mentioned about the farm inputs and the the lowering of cost a few weeks ago we saw the president relived some taxes to employees and small-scale traders or entrepreneurs but how about the farmer can we see our farmers uh being duty free on anything and taxation from anything that they produce and see them what they have produced comes to the market and in fact even the buying of the or purchasing of the farm produce how they are bought now i think uh the the biggest challenge we have with kenya is a production cost is very high look here rise from pakistan i i cannot even imagine the distance from pakistan to nirobi and the distance from muaya to nirobi how comes that rise from that is cheaper in kenya than the one from muaya uh then uh when producing this rise in muaya why does it become so expensive towards to all the costs that betris has mentioned especially on transport like for example uh transporting a banana from a village in meru to a place where it can be picked by parcel vehicles will cost you a distance of three kilometers about one bunch of banana with a hundred pieces of bananas will cost you 150 shillings that's an extra 1.5 shilling per banana now if you place it on a tamak uh apokandwa then you pay 200 shillings for the same banana at distance of 242 kilometers from meru to nirobi remember the 200 shillings is to 42 kilometers because there is good road but now from the village to the tamak here two or three kilometers you're paying 150 shillings so that's the first point and whatever is making the cost of production go high and that's the transport cost so what would the government do about it now you see he kenya maneno niku experiment if we have to move forward as a country we need to experiment and we need to change so many things why should we have a ministry of agriculture and a ministry of roads we have kera we have kura we have kenha let that when maybe the president reorganizes his government or forms another government let's have a ministry for agriculture and rural development so that the minister for agriculture will know i'm responsible for growing tomatoes and i'm still responsible for transporting these tomatoes to the nearest market but as long as the roads are to meachew kwa juhi niya mca niya mp niya nani the cost of production in terms of transport will go high cost of input zero rating of some products in terms of taxes we have 16 percent vat and we have 14 percent vat now after the review the amendments of the act but now Hillary why do we tax farm input we were talking about PPEs yes he's on bazo protective gears kufait corona because we feel the doctors and the nurses are kufront line so we want to zero rate the PPEs so that they buy them at a very cheap cost kitia shilingi miha moja watauzua 84 which is good 16 bobimi on doleo now farmers tunawadara usana wako front line pia uyu nasakitoko hospitali akitoko hospitali atakula ni ni atakula yo PPE zero rate is a chemical spanna fatalizer should be zero rated but we don't want to zero rate them because the government has no capacity to provide fatalizers they want uh kattels to import those fatalizers and sell to farmers da was a kununuzia sometimes you just find a farmer somewhere who had started very well but this person cannot afford the pesticides and the insecticides because they are very expensive so a produce that would avant maybe 50 000 it a chap on our doodoo and the person hands are 30 percent and now the biggest challenge we have and why we are importing everything from toothpicks so let me give you a very practical and sad example people think we are eating pineapples from vika del monte they are very expensive you can't buy we are eating pineapples from tanzania no way my eye my eye li kwa yellow to kill our total if so we can't differentiate between the the egg white and the yolk which is to be yellow we are eating eggs from Uganda I have nothing against Uganda in East Africa we have the free trade policies true that allow us to to do inter country trade which is very okay and our economists in the minister of agriculture know that but now how comes that Uganda is not importing eggs from us we are importing from them reason you have the feed chicken feeds they are taxed let's it's by the size what will I do I'm not seeing as a person but what as a country those who have positions at this particular time what they should do let them make it more friendly to buy our own so that we stop buying from Uganda if we can manage to sell the same crate of eggs at 200 shillings there will be no need of buying importing eggs if we manage to sell mananasi from Delmonte or other local farmers in Muranga at that shillings there will be no need of importing from tz or anywhere reason the prices are at par but currently to produce a bag of maize in Uganda for example you you need like 1200 shillings so if you sell at 1500 let's say if you sell at 1800 you already have made a profit of 600 holding several factors constant 600 out of 1800 that is 33 percent profit which is very good profit but now in Kenya to produce a bag of maize and I find a carisacha praki dog utaona it was a 23 100 300 out of 23 100 100 that is one over eight that's like a 12 percent profit so the government should zero rate most of the basic food production firm inputs so that farmers will access them at a cheaper cost the government should combine the ministry of agriculture with rural development they can even start with counties where we produce a lot of foodstuffs so that those areas get good roads under the ministry of agriculture then zero rating of those chemicals and farmers will produce a bag of maize at 900 shillings why should I buy from ug or import from tz and I'm not telling the president there from Uganda or tz we are not importing it's called free market the prices will regulate themselves all right apparently we are running out of time but I want us to finish with maybe this one he mentioned of importing he has mentioned of Pakistan rise in Kenya now why are we importing things that we are producing here as a nation how when will we bring it to an end and what do we need to do as a nation to ensure our farmers are able to produce we are getting our own even after zero rating the products what else can we do some of the policies that have might have been missed in the formulation all right my thinking is make imported products especially food as expensive as possible that gives the person who is selling local products at an advantage for example an apple from Egypt could cost you maybe 60 shillings but maybe an apple from limuro a river it can be 20 shillings so what will happen we will encourage our own to eat our own produce but you cannot blame the local farmer from selling that same local apple at 70 shillings like he has said because of all those various factors right so the only way to encourage that that's one but but also we need to think about purchasing power you see when you have an employed individuals how are they even able to even afford to buy the local product but if you had good purchasing power what happens even if you bring the imported at 200 shillings but you know the the the muaya rice the pishori is better and it tastes better I can be able to afford that and and and and boost the local local production but all in all we need to move from self-loathe and self-hating and we say why don't we like you say in zero input zero zero taxation on all input of farm of farm products that are good use for production and then once we produce surplus we can make money in our exports right because most likely we can produce quality rice such that we can overproduce to the point now we are selling to people and because we are producing the people say our tea is the best our coffee is the best right meaning ourselves are good and very and very uh full of good minerals isn't it why don't we take advantage of that because trust me even other developed countries that's what that's the same thing they usually do towards us right the tech is very expensive before they are selling to us so why can't Kenya also market itself strategically internationally and put put our farm produce out there to promote the local farmer right uh but the only thing is that we allow ourselves to be bullied uh by by by international market we allow ourselves to be bullied I would say we as Africans always allow ourselves to be bullied and I think it's a high time now we stand and say no no more bullying we produce the best we we offer the best why we will buy at this prices because something else I realize the the international market also affects how the the the potato is going to cost in our country one way or another or even the tea juicy I realize that a farmer is being paid 15 shillings a kilo for tea but when you go to the forex exchange it's at 215 shillings so I was asking myself that farmer has done everything has labored only for someone else to eat 200 shillings there at the foreign exchange also we look at the foreign exchange so when you look at the foreign exchange how can the government come up with policies to ensure that the foreign foreign exchange is favorable to the farmer and it cushions also the farmer so that irrespective whether the dollar is falling or rising the farmer is not affected in one way or another uh until we go to that direction whereby we start believing in our own and start seeing the potential in our own and start putting up structures and policies to promote our own for the benefit of our own then we still have a very long long long long way to go long way to go and let's start also uh marketing uh you see there is also poor marketing of of local farmers produce when I go to the supermarket and I see a pair from South Africa I want to associate myself with that pair instead of me associating with that pair produced in Nimuru and seeing that it's a very good product so also the lock we need to start marketing local farm produce so that Kenyans can actually go for the Kenyan product before they even go for the Ugandan product irrespective whether the price is high or low but for once we start promoting our own and over time we'll have a lot of mass production and even the cost the price will actually go low maybe that's what maybe Uganda have done because fire inform was the the the farmer in Uganda uh before they even export to Kenyans they've already eaten as many eggs in their country right final ones please now if you want to borrow a bazaar in Kenya or you want any kind of help in Kenya and you ask the careers or the profession of your parents in Kenya if you want to get it you rate farmer so that you look poor so unless you move from that aspect of thinking the Kenyan farmer is a poor person we shall not grow and the government the people the officers mr. X mr. Y mr. Z mr. what in the ministry of agriculture they should forget the policies gathering dust on the shelves research come up with new policies that will boost the agricultural production of this country and the people the senior officials in the minister of agriculture should support the president in achieving the big four agenda of food security as a btri said nutritious and sufficient that the country will produce be able to store with those phd ho does in post harvest technologies and i believe zero rating of agricultural farm inputs will help us sort the issue of food and once we have enough food if we get another pandemic like corona lockdown for 21 days we have enough food after 21 days everybody has been tested people have been isolated we treat them and life moves on after 21 days to speak a 50 10 thank you your final words my final words is that a hungry man is always an angry man that's one and if if government wants economic sustainability in the future even the cost of health has to go low for us to have economic growth and food security is one aspect of bringing even the costs of health care lower and as much as we have different ministries i believe in what you call intersectoral partnership so let's not have ministry of agriculture independent ministry of health independent ministry of roads independent but they should all be in tough interdependent that is the kind of mentality and attitude we should go towards last is that we need to also move from the mentality that when you go to do farming you are a poor person that should be killed like yesterday we agree from the king to the poor person we all need to eat hunger pangs cuts across everybody so it's the high time we come up with with projects and programs the way we are saying many youths are unemployed why don't we convert most of these semi arid and arid lands into irrigation schemes and employer youth in those in those sector they'll they'll produce more food yes they'll create employment yes and it's even sustainable in the long in the long run right so and i'm very grateful to see youths in wajil and mandera moving from pastoralism to you know actually domestication of plants and domestication of animals which is improving the health in the wrong land and also when you improve your health in the wrong land what happens you reduce even your expenditure even in the household level it goes lower out of pocket expenditure already goes very low so for government if food is number one and there is actual sustainable food for each and every family then all these others like security people fighting why do people fight because a hungry man is an hungry man they will fight there will be violence people will be falling sick right now and then right so once food is sorted i can guarantee you everything else starts falling into place all right thank you so much bitris and job for coming and sharing your ideas and opinions on how you feel our country should move forward in terms of food security and bakum thank you so much for keeping us company and by the way have you heard of ajira program web application i'll be speaking to you about that in a bit but for now we uh finish what we have we had here she has been my guest bitris kairu health researcher and job mogira he's a uh researcher research analyst thank you so much for keeping us company my name is dereva hillar we see you in a bit and sharon from kiriti thank you so much for watching we appreciate you thank you


Hello and welcome. My name is Shannon Kemp and I'm the Chief Digital Officer of DataVersity. We'd like to thank you for joining this DataVersity webinar, Better Data Governance for Responsible AI, sponsored today by Google Cloud. Just a couple of points to get us started. Due to the large number of people that attend these sessions, you will be muted during the webinar. For questions, you'll be collecting them via the Q&A panel. And if you'd like to chat with us or with each other, we certainly encourage you to do so. And just a note, Zoom defaults the chat to send to just the panelists, but you may absolutely change that to network with everyone. To find the Q&A or the chat panelists, you may find those icons in the bottom middle of your screen. And as always, we will send a follow-up email within two business days containing links to the slides, the recording of the session, and any additional information requested throughout the webinar. Now let me introduce to you our speaker for today, Shuba Law. Shuba is a Senior Program Manager and Data Governance Lead for AI Machine Learning at Google Cloud. She is an established leader, experienced in driving large-scale, ex-functional risk and compliance initiatives, including iData Governance for Google Cloud AI Machine Learning, alphabet-wide programs for regulatory compliance, standards compliance, risk and controls, and internal audit. She has a background in software engineering and a passion for accelerating business through compliance and risk-aware decision-making. And with that, I'll give the floor to Shuba to get today's webinar started. Hello and welcome. Thank you so much, Shannon. So I'm really excited to talk about some of the offerings that Google has in the areas of data governance and responsible AI, our approach to responsible AI and data governance, as well as share some of the learnings that we've collected based on our experience with data governance and responsible AI. So without further ado, let's dive right in. I'm going to start with just some questions that we have been hearing our customers ask us regarding especially AI data use for generative AI, especially in the recent times, but also generally just data as it's relevant to AI. Some concerns that we have heard from our customers are around the areas of, well, data is my differentiator, and certainly that's true. And, you know, how do we protect, how do I protect my intellectual property when I am using data for customizing foundational models, or even for other situations where I am actually using data in the context of AI, even if it's not specifically for generative AI purposes, which is where foundational models tend to play a role. They also are very concerned about ensuring that their data is secure and compliant, especially as they are using data for training models for AI and other purposes, really, who has access to my data. That's an important question that we hear as well. And then, of course, we hear questions around cost effectiveness, not necessarily related to data, but generally still a concern that we've seen our customers express to us. And then, of course, questions around how do I ensure that in the space of AI ML, we are actually mitigating harms. And what I wanted to do is start off with these questions because we take a very holistic approach to data governance at Google here. The first thing that I want to point out is that data governance and responsible AI are both pillars of a larger enterprise readiness offering that we have here at Google.  It covers many topics. We've boiled it down to four pillars. And, of course, these pillars include data governance and privacy, security and compliance, which is a big one, reliability and sustainability, and also safety and responsibility. In today's talk, I'll be taking us through two of these pillars, the data governance and privacy pillar, and also the safety and responsibility. So, let's start with our very first topic, which is the data governance and privacy pillar. But before actually getting into that, I do want to start with just a little bit more about enterprise readiness and why it's important. We actually take it very seriously here at Google, and it's really at the core of our approach to generative AI and just AI in general, enterprise readiness. So, you know, you might ask, why should I care about enterprise readiness? Why is it important? And I think the key thing to remember here is that at Google, we actually want to ensure that through our efforts in the area of enterprise readiness, we are protecting your data as in customer data. And we are only ensuring that your data is used in ways that you intend for it to be used, especially in the case of AI ML development, that we ensure the safety of the data that we get from our customers so that it is secure, and that it is safe from, you know, our applications are safe from threats. So generally speaking, the area of enterprise readiness is critical to helping our customers build high quality, secure machine learning applications. And with that, let me start to dive into the area of data governance and privacy as it pertains to AI ML. So let's start with how we enable our customers with their data governance efforts and privacy efforts. The first and most important thing to remember about this enabling and offering that we have is that we actually really want to make sure our customers know that their data is their data. So once again, your data is your data. In the context of generative AI, what your data includes are input prompts, model output, training data, all of these, and we believe that these are part of your data and your IP. So we take lots of measures to ensure that we do not use our customers data to train our own models, that we process customer data in ways that are in accordance with their instructions. And we actually go a step beyond. We were the first in the industry to publish the AI ML privacy commitment, which outlines our belief that our customers, meaning you should have the highest level of security and control of your data and how it's used for AI ML. Now, I would be remiss if I didn't talk about data governance, if I didn't talk about privacy and security in the context of data governance. So I briefly touch on the fact that we have instituted lots and lots of best practices around privacy to ensure that we are in compliance with regulations like the GDPR. And also that we have extensive controls to protect our customer data from other customers, users, attackers, and any kind of unauthorized access by Google employees when their data is being used for AI ML development. So let's take a closer look at how data, your data especially is used for generative AI. So the first question that I get from customers is what happens to the prompts that I send. These are prompts that are inputs that are provided to the generative AI and a response is generated. So typically that's really how the prompts are used both the input and the output are always encrypted in transit. Then next often I hear from customers the question about how do I customize my models when I'm using AI and how does my data then get used in the process of customization of the models. So the answer there is yes, our customers are enabled to customize their generative AI applications through model tuning. And what you can do is that when you do model tuning to train, you actually train and adapt a model. An adapter model is a model that works alongside the foundation model. It is trained on the customer's data, but importantly, it is only accessible to the specific customer who actually trained that adapter model. So adapter models are fully controlled by the customer, including who can access them and when to delete them. Not only do you control your data used for adapter model training, but your input data is secured at every step of the way as well. And it's also important to note that we do not use customer data logs or any of those additional information to train our foundation models by default. Moving on, I'm going to also quickly touch upon effective data governance and our approach to data governance. So effective data governance and privacy requires a multifaceted approach that starts with a clear set of principles. And privacy first design tools and processes to ensure adherence to those principles. And what we've done is we've developed policies internally regarding the use of data for AI ML development. These policies address both the use as well as the handling of data for AI ML. They also ensure that data classifications are available and guidance is provided to our teams about how to classify their data. What kinds of information about data is important to retain so that they can that information about the data that then can be used to conduct our reviews for data governance purposes. And that, you know, we have the appropriate set of controls to enable protection of data, any data, including customer data during model development and inference. Effective data governance also requires prioritizing data privacy and security. So we do build the strongest security technologies into AI products so that we can ensure protection of this data and appropriate controls as data is used for AI ML purposes. Our approach also leverages Google Cloud's privacy experience and incorporates privacy by design principles. Such that designing AI products and services happens with privacy safeguards right from the very beginning. We offer tooling and solutions like the cloud DLP or data loss prevention tool, which ensures that we can redact data as it's used for AI ML or any purpose for that matter. We also offer content filters and recitation checks that basically enable us to improve our data governance and privacy efforts as well as we offer these tools to our customers so they too can leverage these same tools to improve their data governance and and privacy efforts. We also provide transparency for customer data usage and assist enterprises in their data protection impact assessment efforts with our DPIA resource center and documentation. And we've achieved many privacy and security certifications such as ISO 27001 and compliance attestations from various different independent auditors who've assessed our practices giving you the confidence that you need around our data governance approach and our practices. So when setting up your own data governance program, I want to leave you with three actions that you can take right now as leaders. Number one, record and manage your data provenance. Now this is really important. It's important because it sets the foundation for data governance for AI ML.  So it's very important therefore to know what data sets have gone into training which version of the models. So keeping model lineage and versioning straight as well as specific versioning around the data sets that actually have gone into training those particular checkpoints is absolutely key. It is the foundation on top of which then your data governance for AI ML programs can be built. My understanding would be that even without AI ML, you already have some of the data mapping data flows already understood for your for your organization, but for AI ML purposes, we also want to start tracking data provenance as as data is used to develop models. The next thing that I would focus on is to establish policies for data use classification and handling. So this is key as well. What kinds of how would you classify different kinds of data that your organization uses? Is it user data? Is it customer data? Or is it some other kind of data? And when you classify these data sets into various different classifications, what are the approved use cases to use those data sets? Who is going to have access to those data sets when they're used for AI ML and providing guidance to your engineering teams and product teams is key. And establishing policies allow you to actually publish your your stance on on how this data should be used or could be used as well as how it should be handled. Now, one idea is to actually publish policies and the next is to actually uphold these policies through actual AI governance procedures. So setting up some kind of procedures, reviews, processes to enable that these the to enable the use of this data in ways that upholds the policy is also equally important. And then lastly, what I would focus on is taking a very risk based approach to managing data, especially it's used for AI ML. Now, as you know, in AI ML, there is large amounts of data sets that can be used to train models. And it's really important when you are using these data sets to a comply with the policy, but also be aware that especially when there is a huge amount of scale. And there is a lot of different use cases to be able to have some way of prioritizing those that are kind of going to be the most highest to risk or potentially the highest risk. And also mitigating those risks, and then aligning with leadership risk tolerance levels on on taking a going forward with a use case or not. So having a risk based approach actually ensures that when using data and handling data that it's actually done in alignment with leadership risk appetite as well. Moving on to the fourth pillar for of enterprise resident readiness, which is safety and responsibility. I'll start with a little bit of history here five years ago. We were the first companies, one of the first companies to publish. These principles were crafted with input from customers and partners. And they're at the core of how we design and operate our generative AI products. We work with our customers to collectively achieve the intended benefits and avoid unintended potential harms of these amazing technologies. We operationalize these principles in three ways. Processes to review products and use cases. Tooling that's available both to our internal teams, as well as to our customers and partners. And third through industry leading research and best practices. So let's take a look at an example of our review process. When we conduct a responsible AI review, we follow three steps. First, we identify potential harms. Then we assess the risk levels. And last, we develop mitigation plans. One of our customers wanted to use image analysis to help categorize images in a way that would help to flag content that might be offensive to users. Enable better editorial decisions and to enable better ads targeting. We helped the customer identify potential harms both in their business roles and in potential bias in the image class of suppliers. We assess the risk levels that led to a prioritized list of medications to establish fairness testing for sensitive categories and subgroups. And ultimately, all of this resulted in a better experience for their users and a higher confidence in the implementation. So conducting these responsible AI assessments is a skill that can be acquired in many ways. It's different than standard decision making. And we welcome the opportunity to engage with you to conduct one or more responsible AI assessments to help you launch better products and to impart some of our experience onto your organizations. Tooling is also a key area of leadership for us. We were one of the first to deploy the automated adversarial testing and are consistently leading the industry on content filters and checkers. Let's take a closer look at some of these tools starting with safety filters. Here's an example output from our safety filters. We classify across 15 potentially harmful categories and provide a confidence score of zero to one so that you can set safety filters that are appropriate for your use case. The next one is recitation checkers, which are another important tool. They help to ensure that the outputs from your generative AI applications do not replicate existing content. Our state-of-the-art recitation checkers work across text, images and video and run with a low latency that's needed for modern applications. At Google Cloud, we continue to invest in best-in-class processes and tooling for responsible AI. We tap into the best thinking and innovation from our Google research teams and also work with third parties from academia, government and industry to ensure that we are bringing to you the best, most comprehensive solutions to ensuring your applications minimize potential unintended harms and maximize the intended benefits for your users. I want to leave you with three actions you can take right now as leaders that are in the responsible AI area. The first one is to be an active and visible sponsor for responsible AI. Responsible AI actually is important from the standpoint of generally ensuring that the applications that we are creating are going to be useful and truly achieve the intended benefit. There have been a lot of concerns and questions around data quality as it's used for different kinds of AI applications. One of the key things to really pay attention to is ensuring that our data is actually fully representative. It's free of bias and that it is actually being prepared very carefully as it is being used to develop models. There needs to be an understanding as we are using data to develop models that needs to be shared and consistently understood across the organization in order to ensure that this data is used responsibly as we develop AI ML. And we create applications using this data. The second is to establish AI governance processes if you've not already done so. Here what I'd like to point out is that at Google what we do is we have processes through which we conduct these reviews like the one that we were talking about earlier in this talk that enable us to actually ensure that the applications that we are developing are actually living up to the principles that we have set forth for the development of AI in a responsible way. So oftentimes it is helpful not only to publish these principles or internal policies that we have around data governance but also to have teams who can actually conduct reviews and provide guidance on specific use case by use case basis to ensure that these principles and these kinds of policies are actually being implemented and upheld as AI is being developed. And then lastly, we also want to build responsible AI capacity and capabilities across teams and throughout the organization. It is really important to actually have teams that work cross functionally to make these principles and policies actually a reality. So not only is it an effort of say the compliance teams, but it actually needs to be a very joint effort, a collaborative effort between your privacy teams, your security teams, legal teams. Of course, product teams who are consisting of both product management, engineering, and of course the data practitioners like yourselves to come together and as a collaborative effort, ensure that when designing these kinds of applications that we are actually considering all aspects of both use and handling of data and also the resultant products that are going to be created in terms of models and how they'll function as a kind of collective picture before we actually make decisions on how we move forward. So it's extremely important to actually bring together just a set of cross functional folks who can collectively provide input and help make decisions that can drive towards better, more responsible products and also handling and use of data in responsible ways. In closing, I'd like to ensure that you are aware that enterprise readiness is a topic that we take very seriously at Google, especially at Google Cloud, and that our generative AI products have been built with all of these four foundational pillars in mind. To give you the confidence that your data is only being used in ways that you intend, your applications are safe from threats and always available to deliver the value that your users are expecting from you. Thank you. And I'm happy to open it up to questions. Shiba, thank you for this presentation. If you have questions for Shiba, feel free to submit them in the Q&A portion of your screen. So, and just to answer the most commonly asked questions, just a reminder, I will send a follow up email by end of day Monday with links to the slides and links to the recording of this session. So, Shiba, can you elaborate on enterprise readiness? What does it refer to exactly from a technology perspective and security? Yeah, so enterprise readiness is really a concept that is a collection of various different areas that need to come together in order to ensure that our customers are able to rely on the offerings that we have here at Google to develop applications that are high quality, and are secure. So what that means is that there are offerings that are already available through the Google Cloud infrastructure, including the security offerings that we have, the data governance work that we already do and assurances that we provide to our customers, the responsible AI work that we do to ensure that our customers actually can leverage all of these existing capabilities when they're building their applications without actually having to reinvent the wheel or create these themselves. So there is already a rich offering of these kinds of services that are already provided that enable our customers then to go off and focus on what is really important, which is how to use their data to train their models for their specific use case without actually having to worry too much about the underlying infrastructure that is already in place and offerings that are already in place to enable you to develop these applications responsibly. These are the same applications or offerings that will also enable you to actually get a jumpstart on your own data governance initiatives, on your own privacy initiatives, and your own responsible AI initiatives as well, because there is tooling that is being offered, whether it's in the form of DLP, or in the form of various different recitation checkers, etc., that not only are being leveraged by us in order to develop our own models, but are also being offered to our customers so that they too can take advantage of these tools and use them as part of their data governance and responsible AI efforts. Perfect. And what are the steps to become a data governance? So the steps we talked about are, you must start with having a really good understanding of what your data sets are, what the flows are, where they exist, and such. Now, this is not new to AIML. This is actually a standard practice that's required anyways for your own privacy compliance and such. But for AIML, I think the unique and interesting twist that AIML presents is that in addition to your data mapping, what you want to do is also ensure that you have a very good understanding of how data is being used and what versions of data sets are being used for developing which model. So tracking your model lineage, tracking your data provenance is, I would say, the first step to really understand which data sets and which versions of data sets are being used to train which checkpoint. So that's the unique and new thing, the AIML, that you might have to do in addition to your already existing data governance efforts that include data mapping. On top of that, as a company, you really, or as an organization, you really need to decide what policies and guidance you want to put in place with regards to the use and handling of data. Some of this may already be in place for generally data governance efforts, but for AIML specifically, there might be some unique nuances and twists that come about as a result of using data to train models that need to be comprehended as part of your policies and frameworks that you put in place specifically for AIML uses and handling of data. So that's the next step. And of course, in order to uphold these policies and procedures, policies, you would need to then put in place some procedures and processes that would ensure that appropriate practices are put in place to enable the compliance with these policies and once you've actually gotten that going, then you would want to start to think about really monitoring data use and data handling and really starting to think about how this is not compliant with existing policies, how it is that policies actually need to maybe expand in scope or address some of the new use cases that are coming your way as you're noticing and then continually evolve through an iterative approach the policies that you've put in place as well as continue to evolve your data provenance efforts as well. As many of us know, this is a fairly nascent space, lots and lots of new things are happening in the space of AIML. So it's quite natural to expect that your policies will evolve especially with changing business needs and especially with just the way the space is evolving as well and also your procedures will need to evolve accordingly as well. So I would continually iterate on the approach but I would start with these steps and then and then go ahead and continue to iterate. Perfect. Thank you. So and Shiba, how do data governance, AI governance, IT governance and corporate governance intersect and or complement? Yeah, so there is a set of organizations in any company who will be going forth with with data governance related efforts. My experience has been that when you set policy, it is actually very important to ensure that any policy that is being created or published is actually getting approvals and alignment from any relevance stakeholders and usually this needs to be a very broad set of stakeholders that come from various different areas and different functions within the company so that any policy that is being created is actually aligned with existing policies, whether it's in the area of security or privacy or say another organization so that all your policies together form a web that actually provide guidance collectively to an end users. You definitely want to avoid any conflict in policies. There shouldn't be that and also that policies ensure that policies are actually complementing each other. So if there is a policy that addresses a privacy, then the security policy should jive well with it and they should actually point to each other. Same thing with your data governance policy as well. And typically what happens is that in large organizations, you might have an overarching policy, that is say a company wide policy, but then that policy actually makes room for smaller divisions in the company to come up with their own policies that are unique to those particular divisions and speak more to the specific needs, the specific product needs or the specific function for that particular division. But always those policies need to be in alignment with the larger overarching policy as well. So I think the key message is that not only do you want to have your teams who are your compliance teams collaborating with each other and collectively leveraging each other's work as they conduct their specific reviews, but their policies should all be in alignment with each other as well. And that truly is the kind of building block, the first step that as you're setting your policies that you want to address in order to ensure that your efforts in the data governance space and the larger data governance space are all aligned with each other. Thank you. And so many great questions coming in. Feel free to put them in the Q&A portion. And so, Shuba, I agree that a key objective is to ensure that data utilized in AI model is free of bias. What methodology or best practices do you recommend to achieve this goal? Yeah, so ensuring that data is free of bias is something that our responsible AI team actually looks into. It is actually done with a really good understanding of the data and also with some tooling and testing that is done to ensure that we are really looking at the data and making it as representative as possible. It is covered as part of our reviews of data as data sets are used to train models. And a lot of this work is actually based on research that has been done that has enabled us to really develop the tooling processes that are necessary to eliminate bias from our data sets and from the models that are resultant from those data sets. Thank you. And what are the differences between machine learning governance practices versus generative AI governance practices? Yeah. So machine learning practices in the past, we have dealt a lot with classification type of models, models that have been able to predict, for instance, certain predictive models for certain use cases and such. And those have been areas that we've actually looked at various different risks as well. But with generative AI, the big difference that I see is that generative AI tends to actually be very heavy in the use of data to train models. So we're talking about large data sets that are used to train these models that then can actually generate results. So the risk that these models present is actually quite high from two aspects. Number one, that the output of the models, these generative models can be quite imaginative out of the regular parameters that you might set for a classification model. So an example would be if you have a model that is specifically going to classify certain text as, say, a certain category and provide you a category. That output is actually pretty limited in its range to the categories that the generative model, the classification model is going to be able to output. Compare that to a generative model, which can actually have a much wider range of outputs from which it can select because it's generative, and that you may not be able to even predict in advance. So in terms of the outputs that these generative models can provide, there is a huge range and that huge range then gives a lot of freedom to the model to provide you an output that can come from anywhere in this large range. And what that typically means is that you have to then take additional measures to ensure that these outputs are free of any kind of recitation that the models were trained on and other kinds of controls that you need to put in place so that these outputs are not going to essentially create any unintended consequences than what the model was intended for. So that's one. And the input side, these models are actually taking huge amounts of data to train these models, even the foundational models. So one has to be really careful to look at these data sets and understand all the risks that come with these data sets and ensure that these risks are actually mitigated as we are training these models. So both on the input, as well as on the output, there is a larger risk on the generative AI models than there is on regular machine learning type of models. And one has to account for those both legal as well as business risks when when thinking about generative AI models. Great information here. Lots of questions coming in. We've got about 10 minutes left to get through the as many questions as we can. So Shiba, let's say I've deployed a LLM foundation model, which obviously pre-trained. What technique would you recommend to manage model bias and model drifting? So I am not an expert in the specifics of model drifting and model bias, because this is something that our responsible AI team handles. But I do know that both of these are areas that we look at at Google for our own models. And these involve usually reviews of these models and appropriate monitoring of these models to ensure that we are actually enabling these models to be both unbiased. As well as free from drift. And how do you handle the people culture element of data governance? What are the learnings of this to best implement AI? So I think that the key here is that the culture of our teams needs to be just as inclusive and as aware of the kind of issues that we've had. With models and AI in the past to ensure that we are able to model the kind of diversity and representativeness that we are looking for in our development teams as much as possible as well. So I think our teams here from a responsible AI perspective are actually very carefully looking at various different happenings in the world. They're aware of what's happening outside of Google as well. And looking to incorporate all the learnings that we have regarding the issues that come up with all kinds of fairness and bias issues as well as just kind of social harm types of issues to ensure that when we are developing our models, that we are actually comprehending these and taking measures to avoid them in the functionality of our models as we are developing them. And not only that, but also helping our customers develop models in ways that they can work through as they were intended. So it's always an effort of ensuring that our ethicists are involved in the development of our models, ensuring that their input is heard and it's actually implemented. And that ultimately our models are free of these kinds of biases or any kinds of issues and also in alignment with our own leadership's interest in ensuring that these models behave the way they're supposed to behave. And what are the steps to become a data governance from a technical point of view, so programming language, any specialization on a data cloud storage? So I would say that it is a field that is quite diverse in the kinds of people who I've seen become data governance practitioners. I have found that some kind of technical background is actually quite helpful. So in my case, for instance, having written some software myself in the past has been helpful in being able to understand how the lifecycle works and how data is used in the development of these models and such. So I think that is helpful. And then I would say a good amount of risk and compliance background is also very useful. So between having some kind of technical knowledge of the field itself, as well as risk and controls background. I found that most of the folks I work with have some combination of those two types of skills from a data governance standpoint. Now, in the responsible AI realm, I have seen many more folks who have backgrounds in ethics and such. But generally speaking, I would say that the ability to manage risk and compliance is somewhat evident in the work that we do and a good skill to have as you think about career and data governance. I love it. So what are the three most important things employees should know before using and relying on enterprise AI? Well, I'd say that some of the key things that anybody should be aware of as they are looking to use their data sets to train models is number one, have a really, really good understanding of the data itself and make sure that you understand how the data is structured, how representative it is. You've done some kind of data visualization and such to ensure that you yourself have a fairly good understanding of the data. Secondly, I would say that as you're using Google's enterprise readiness offerings to train your models, make sure that you are actually aware of the different kinds of offerings that are already there and you're using them appropriately. So look, having access to a data loss prevention tool so that you can actually redact the data sets of PII as you start to use them for model training and what have you. Having access to these tools and being able to use them so that you can appropriately design both what your input as well as what your outputs would look like with the help of these tools is also, I would say, paramount. And then lastly, I would say that as you are training models and you are developing these models, really understand the needs that you have for the organization. What is the specific use case and what does it really warrant? Does it warrant a custom model? Does it warrant just regular model tuning? Does it warrant just needing a foundation model that you can feed your prompts to? Really understanding what the architecture should be, what types of models you really need to create or use out of the box and then how to deploy them, making use of all the offerings that are already there to help you ensure that you are developing models that, A, are using data in a manner that is comfortable to you and is in line with your policies and two, also is functioning the way you intend it to function. Perfect. And I'm going to ask for just the elevator pitch here. We've got just two minutes left, but I'm going to slip one more question in. Since generative AI or any other AI subsets could generate stunning outcomes, you might cover about auditing is the most important to make sure enterprises do not take the outcome as is. You cut out there for a moment. Shannon, do you mind repeating the question? Sure. Yeah, since generative AI or any other AI subsets could generate stunning outcomes, I expect you might cover about auditing is the most important to make sure enterprises do not take the outcome as is. Yeah, so I think what we're talking about here is different kinds of checks that we have to ensure that your outputs are being then filtered. So we talked about some safety filters that will enable your outputs and depending on the thresholds that you set can enable your outputs to be free of certain kinds of parameters, that you actually will set. So certain kinds of things that you don't want to be output can actually be set with those kind of recitation filters and such. And the other thing that you're talking about with respect to auditing, typically what happens is that there are standards that we as Google would also comply with. And in order to meet those standards, we go through our own external audits to ensure that we have developed our own practices and processes in compliance with certain standards and certificates that we would certify to to provide assurance to our customers like yourself that we've met certain criteria and standard for privacy and security for our product offerings as well. Thank you so much for this and thanks to all of our attendees for all the great questions and being so engaged in everything. Just again, a reminder, I will send a follow-up email by end of day, Thursday for this webinar with links to the slides and links to the recording. Thank you all. Thanks, Shiba. Thank you. Hope you all have a great day.