<|begin_of_text|><|start_header_id|>system<|end_header_id|>You are an expert on climate change, human mobility and migration. Answer the question truthfully.<|eot_id|><|start_header_id|>user<|end_header_id|>Here are the title and abstract of a scientific paper probably related to climate change, human mobility and migration..
Title:"Auditing the fairness of place-based crime prediction models implemented with deep learning approaches."
Abstract:"Place-based crime prediction models implemented with deep learning leverage the spatio-temporal patterns of historical crimes, together with built-environment factors, to predict aggregate volumes of crime incidents at specific locations. Nevertheless, related work has shown that crime incident data can suffer from under-reporting bias whereby certain social groups report more crimes than others and whereby certain crimes are recorded more often by the police. In addition, under the umbrella of crime opportunity theories that suggest that human mobility can play a role in crime generation, increasing attention has been paid to the predictive power of human mobility in place-based crime models implemented with deep learning. However, prior work has shown that human mobility data collected from cell phones can suffer from sampling bias because certain population groups are not equally represented due to lack of cell phone ownership. Despite these well reported data bias concerns, no related work has yet looked into the fairness of place-based crime prediction models implemented with deep learning; nor into the effect that adding human mobility data from cell phones as a predictor might have on their fairness. In this paper, we use publicly available fine-grained crime incident and human mobility data from the US to conduct a comprehensive fairness audit across multiple cities with diverse demographic characteristics, different types of crimes and various deep learning models; and we explore potential root causes of the models' fairness metrics by looking into crime data bias, mobility data bias and algorithmic bias. Our results show that unfair predictions are pervasive across place-based crime prediction models implemented with deep-learning due to the inherent data bias in the crime incident data; that adding mobility features extracted from cell phone data decreases fairness under certain circumstances; and that part of the fairness loss can be explained by bias in the crime data, and in the predictive algorithms that might be exacerbating the data bias, with the mobility data bias having an insignificant effect on fairness loss.."
I have six questions you need to answer.
Q1: Is this article is about 'human migration mobility': 1 = yes; 0 = no. Answer with a number. Do not give a textual explanation.

Q2: What drivers of migration and mobility does the article study? 0 = 'other', 1 = 'climate', 2 = 'environment', 3 = 'economic', 4 = 'conflict', 5 = 'political', 6 = 'social', 7 = 'family'. Answer with a number or a sequence of numbers separated by commas. Do not give a textual explanation. 

Q3: What is the overall sentiment of the article?:0 = 'neutral', 1 = 'negative', 2 = 'positive'. Answer with a number. Do not give a textual explanation.

Q4: What type of climate or environmental keywords are explicitly mentioned?:  0 = 'none', 1 = 'algal blooms', 2 = 'infestation', 3 = 'infectious diseases', 4 = 'ocean acidification', 5 = 'sea ice', 6 = 'acid rain', 7 = 'drought', 8 = 'cold wave', 9 = 'heat wave', 10 = 'glacial', 11 = 'wildfire', 12 = 'air pollution', 13 = 'biodiversity loss', 14 = 'coastal erosion', 15 = 'land degradation', 16 = 'deforestation', 17 = 'desertification', 18 = 'water pollution', 19 = 'diebacks', 20 = 'permafrost loss', 21 = 'sand mining', 22 = 'sea level rise', 23 = 'earthquake', 24 = 'tsunami', 25 = 'volcanic', 26 = 'flood', 27 = 'avalanche', 28 = 'landslide', 29 = 'downburst', 30 = 'thunderstorm', 31 = 'sandstorm', 32 = 'haze', 33 = 'storm', 34 = 'cyclone', 35 = 'blizzard', 36 = 'hail', 37 = 'snow', 38 = 'tornado', 39 = 'wind', 40 = 'hurricane', 41 = 'typhoon', 42 = 'other'. Answer with a number or a sequence of numbers separated by commas. Do not give a textual explanation. 

Q5: What discipline does the article belong to?: 0 = 'other', 1 = 'social science', 2 = 'natural science', 3 = 'arts and humanities', 4 = 'multidiscipline and interdiscipline'. Answer with a number. Do not give a textual explanation.

Q6: Does the article apply quantitative and/or statistical analysis?: 0 = 'no', 1 = 'yes'. Answer with a number. Do not give a textual explanation.

Use the following template: 

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Q6 = [].<|eot_id|><|start_header_id|>assistant<|end_header_id|>Answer:

Auditing the fairness of place-based crime prediction models implemented with deep learning approaches
Place-based crime prediction models implemented with deep learning leverage the spatio-temporal patterns of historical crimes, together with built-environment factors, to predict aggregate volumes of crime incidents at specific locations. Nevertheless, related work has shown that crime incident data can suffer from under-reporting bias whereby certain social groups report more crimes than others and whereby certain crimes are recorded more often by the police. In addition, under the umbrella of crime opportunity theories that suggest that human mobility can play a role in crime generation, increasing attention has been paid to the predictive power of human mobility in place-based crime models implemented with deep learning. However, prior work has shown that human mobility data collected from cell phones can suffer from sampling bias because certain population groups are not equally represented due to lack of cell phone ownership. Despite these well reported data bias concerns, no related work has yet looked into the fairness of place-based crime prediction models implemented with deep learning; nor into the effect that adding human mobility data from cell phones as a predictor might have on their fairness. In this paper, we use publicly available fine-grained crime incident and human mobility data from the US to conduct a comprehensive fairness audit across multiple cities with diverse demographic characteristics, different types of crimes and various deep learning models; and we explore potential root causes of the models' fairness metrics by looking into crime data bias, mobility data bias and algorithmic bias. Our results show that unfair predictions are pervasive across place-based crime prediction models implemented with deep-learning due to the inherent data bias in the crime incident data; that adding mobility features extracted from cell phone data decreases fairness under certain circumstances; and that part of the fairness loss can be explained by bias in the crime data, and in the predictive algorithms that might be exacerbating the data bias, with the mobility data bias having an insignificant effect on fairness loss.