Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency最新文献

{"title":"Case study: predictive fairness to reduce misdemeanor recidivism through social service interventions","authors":"Kit T. Rodolfa, E. Salomon, Lauren Haynes, Iván Higuera Mendieta, Jamie L Larson, R. Ghani","doi":"10.1145/3351095.3372863","DOIUrl":"https://doi.org/10.1145/3351095.3372863","url":null,"abstract":"The criminal justice system is currently ill-equipped to improve outcomes of individuals who cycle in and out of the system with a series of misdemeanor offenses. Often due to constraints of caseload and poor record linkage, prior interactions with an individual may not be considered when an individual comes back into the system, let alone in a proactive manner through the application of diversion programs. The Los Angeles City Attorney's Office recently created a new Recidivism Reduction and Drug Diversion unit (R2D2) tasked with reducing recidivism in this population. Here we describe a collaboration with this new unit as a case study for the incorporation of predictive equity into machine learning based decision making in a resource-constrained setting. The program seeks to improve outcomes by developing individually-tailored social service interventions (i.e., diversions, conditional plea agreements, stayed sentencing, or other favorable case disposition based on appropriate social service linkage rather than traditional sentencing methods) for individuals likely to experience subsequent interactions with the criminal justice system, a time and resource-intensive undertaking that necessitates an ability to focus resources on individuals most likely to be involved in a future case. Seeking to achieve both efficiency (through predictive accuracy) and equity (improving outcomes in traditionally under-served communities and working to mitigate existing disparities in criminal justice outcomes), we discuss the equity outcomes we seek to achieve, describe the corresponding choice of a metric for measuring predictive fairness in this context, and explore a set of options for balancing equity and efficiency when building and selecting machine learning models in an operational public policy setting.","PeriodicalId":377829,"journal":{"name":"Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency","volume":"80 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128889703","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Whose tweets are surveilled for the police: an audit of a social-media monitoring tool via log files","authors":"G. Borradaile, Brett C. Burkhardt, Alexandria LeClerc","doi":"10.1145/3351095.3372841","DOIUrl":"https://doi.org/10.1145/3351095.3372841","url":null,"abstract":"Social media monitoring by law enforcement is becoming commonplace, but little is known about what software packages for it do. Through public records requests, we obtained log files from the Corvallis (Oregon) Police Department's use of social media monitoring software called DigitalStakeout. These log files include the results of proprietary searches by DigitalStakeout that were running over a period of 13 months and include 7240 social media posts. In this paper, we focus on the Tweets logged in this data and consider the racial and ethnic identity (through manual coding) of the users that are therein flagged by DigitalStakeout. We observe differences in the demographics of the users whose Tweets are flagged by DigitalStakeout compared to the demographics of the Twitter users in the region, however, our sample size is too small to determine significance. Further, the demographics of the Twitter users in the region do not seem to reflect that of the residents of the region, with an apparent higher representation of Black and Hispanic people. We also reconstruct the keywords related to a Narcotics report set up by DigitalStakeout for the Corvallis Police Department and find that these keywords flag Tweets unrelated to narcotics or flag Tweets related to marijuana, a drug that is legal for recreational use in Oregon. Almost all of the keywords have a common meaning unrelated to narcotics (e.g. broken, snow, hop, high) that call into question the utility that such a keyword based search could have to law enforcement. As social media monitoring is increasingly used for law enforcement purposes, racial biases in surveillance may contribute to existing racial disparities in law enforcement practices. We are hopeful that log files obtainable through public records request will shed light on the operation of these surveillance tools. There are challenges in auditing these tools: public records requests may go unfulfilled even if the data is available, social media platforms may not provide comparable data for comparison with surveillance data, demographics can be difficult to ascertain from social media and Institutional Review Boards may not understand how to weigh the ethical considerations involved in this type of research. We include in this paper a discussion of our experience in navigating these issues.","PeriodicalId":377829,"journal":{"name":"Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126256547","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Interventions for ranking in the presence of implicit bias","authors":"L. E. Celis, Anay Mehrotra, Nisheeth K. Vishnoi","doi":"10.1145/3351095.3372858","DOIUrl":"https://doi.org/10.1145/3351095.3372858","url":null,"abstract":"Implicit bias is the unconscious attribution of particular qualities (or lack thereof) to a member from a particular social group (e.g., defined by gender or race). Studies on implicit bias have shown that these unconscious stereotypes can have adverse outcomes in various social contexts, such as job screening, teaching, or policing. Recently, [34] considered a mathematical model for implicit bias and showed the effectiveness of the Rooney Rule as a constraint to improve the utility of the outcome for certain cases of the subset selection problem. Here we study the problem of designing interventions for the generalization of subset selection - ranking - that requires to output an ordered set and is a central primitive in various social and computational contexts. We present a family of simple and interpretable constraints and show that they can optimally mitigate implicit bias for a generalization of the model studied in [34]. Subsequently, we prove that under natural distributional assumptions on the utilities of items, simple, Rooney Rule-like, constraints can also surprisingly recover almost all the utility lost due to implicit biases. Finally, we augment our theoretical results with empirical findings on real-world distributions from the IIT-JEE (2009) dataset and the Semantic Scholar Research corpus.","PeriodicalId":377829,"journal":{"name":"Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132237286","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The false promise of risk assessments: epistemic reform and the limits of fairness","authors":"Ben Green","doi":"10.1145/3351095.3372869","DOIUrl":"https://doi.org/10.1145/3351095.3372869","url":null,"abstract":"Risk assessments have proliferated in the United States criminal justice system. The theory of change motivating their adoption involves two key assumptions: first, that risk assessments will reduce human biases by making objective decisions, and second, that risk assessments will promote criminal justice reform. In this paper I interrogate both of these assumptions, concluding that risk assessments are an ill-advised tool for challenging the centrality and legitimacy of incarceration within the criminal justice system. First, risk assessments fail to provide objectivity, as their use creates numerous sites of discretion. Second, risk assessments provide no guarantee of reducing incarceration; instead, they risk legitimizing the criminal justice system's structural racism. I then consider, via an \"epistemic reform,\" the path forward for criminal justice reform. I reinterpret recent results regarding the \"impossibility of fairness\" as not simply a tension between mathematical metrics but as evidence of a deeper tension between notions of equality. This expanded frame challenges the formalist, colorblind proceduralism at the heart of the criminal justice system and suggests a more structural approach to reform. Together, this analysis highlights how algorithmic fairness narrows the scope of judgments about justice and how \"fair\" algorithms can reinforce discrimination.","PeriodicalId":377829,"journal":{"name":"Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency","volume":"214 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121240136","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"What's sex got to do with machine learning?","authors":"Lily Hu, Issa Kohler-Hausmann","doi":"10.1145/3351095.3375674","DOIUrl":"https://doi.org/10.1145/3351095.3375674","url":null,"abstract":"The debate about fairness in machine learning has largely centered around competing substantive definitions of what fairness or nondiscrimination between groups requires. However, very little attention has been paid to what precisely a group is. Many recent approaches have abandoned observational, or purely statistical, definitions of fairness in favor of definitions that require one to specify a causal model of the data generating process. The implicit ontological assumption of these exercises is that a racial or sex group is a collection of individuals who share a trait or attribute, for example: the group \"female\" simply consists in grouping individuals who share female-coded sex features. We show this by exploring the formal assumption of modularity in causal models using directed acyclic graphs (DAGs), which hold that the dependencies captured by one causal pathway are invariant to interventions on any other causal pathways. Modeling sex, for example, as a node in a causal model aimed at elucidating fairness questions proposes two substantive claims: 1) There exists a feature, sex-on-its-own, that is an inherent trait of an individual that then (causally) brings about social phenomena external to it in the world; and 2) the relations between sex and its downstream effects can be modified in whichever ways and the former node would still retain the meaning that sex has in our world. Together, these claims suggest sex to be a category that could be different in its (causal) relations with other features of our social world via hypothetical interventions yet still mean what it means in our world. This fundamental stability of categories and causes (unless explicitly intervened on) is essential in the methodology of causal inference, because without it, causal operations can alter the meaning of a category, fundamentally change how it is situated within a causal diagram, and undermine the validity of any inferences drawn on the diagram as corresponding to any real phenomena in the world. We argue that these methods' ontological assumptions about social groups such as sex are conceptual errors. Many of the \"effects\" that sex purportedly \"causes\" are in fact constitutive features of sex as a social status. They constitute what it means to be sexed. In other words, together, they give the social meaning of sex features. These social meanings are precisely, we argue, what makes sex discrimination a distinctively morally problematic type of act that differs from mere irrationality or meanness on the basis of a physical feature. Correcting this conceptual error has a number of important implications for how analytical models can be used to detect discrimination. If what makes something discrimination on the basis of a particular social grouping is that the practice acts on what it means to be in that group in a way that we deem wrongful, then what we need from analytical diagrams is a model of what constitutes the social grouping. Such a model would a","PeriodicalId":377829,"journal":{"name":"Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122908455","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Data in New Delhi's predictive policing system","authors":"Vidushi Marda, Shiv Narayan","doi":"10.1145/3351095.3372865","DOIUrl":"https://doi.org/10.1145/3351095.3372865","url":null,"abstract":"In 2015, Delhi Police announced plans for predictive policing. The Crime Mapping, Analytics and Predictive System (CMAPS) would be implemented in India's capital, for live spatial hotspot mapping of crime, criminal behavior patterns and suspect analysis. Four years later, there is little known about the effect of CMAPS due to the lack of public accountability mechanisms and large exceptions for law enforcement under India's Right to Information Act. Through an ethnographic study of Delhi Police's data collection practices, and analysing the institutional and legal reality within which CMAPS will function, this paper presents one of the first accounts of smart policing in India. Through our findings and discussion we show what kinds of biases are present within Delhi Police's data collection practices currently and how they translate and transfer into initiatives like CMAPS. We further discuss what the biases in CMAPS can teach us about future public sector deployment of socio-technical systems in India and other global South geographies. We also offer methodological considerations for studying AI deployments in non-western contexts. We conclude with a set of recommendations for civil society and social justice actors to consider when engaging with opaque systems implemented in the public sector.","PeriodicalId":377829,"journal":{"name":"Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117223650","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The philosophical basis of algorithmic recourse","authors":"S. Venkatasubramanian, M. Alfano","doi":"10.1145/3351095.3372876","DOIUrl":"https://doi.org/10.1145/3351095.3372876","url":null,"abstract":"Philosophers have established that certain ethically important values are modally robust in the sense that they systematically deliver correlative benefits across a range of counterfactual scenarios. In this paper, we contend that recourse - the systematic process of reversing unfavorable decisions by algorithms and bureaucracies across a range of counterfactual scenarios - is such a modally robust good. In particular, we argue that two essential components of a good life - temporally extended agency and trust - are underwritten by recourse. We critique existing approaches to the conceptualization, operationalization and implementation of recourse. Based on these criticisms, we suggest a revised approach to recourse and give examples of how it might be implemented - especially for those who are least well off1.","PeriodicalId":377829,"journal":{"name":"Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128228165","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Doctor XAI: an ontology-based approach to black-box sequential data classification explanations","authors":"Cecilia Panigutti, A. Perotti, D. Pedreschi","doi":"10.1145/3351095.3372855","DOIUrl":"https://doi.org/10.1145/3351095.3372855","url":null,"abstract":"Several recent advancements in Machine Learning involve blackbox models: algorithms that do not provide human-understandable explanations in support of their decisions. This limitation hampers the fairness, accountability and transparency of these models; the field of eXplainable Artificial Intelligence (XAI) tries to solve this problem providing human-understandable explanations for black-box models. However, healthcare datasets (and the related learning tasks) often present peculiar features, such as sequential data, multi-label predictions, and links to structured background knowledge. In this paper, we introduce Doctor XAI, a model-agnostic explainability technique able to deal with multi-labeled, sequential, ontology-linked data. We focus on explaining Doctor AI, a multilabel classifier which takes as input the clinical history of a patient in order to predict the next visit. Furthermore, we show how exploiting the temporal dimension in the data and the domain knowledge encoded in the medical ontology improves the quality of the mined explanations.","PeriodicalId":377829,"journal":{"name":"Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127451083","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Bias in word embeddings","authors":"O. Papakyriakopoulos, Simon Hegelich, J. M. Serrano, Fabienne Marco","doi":"10.1145/3351095.3372843","DOIUrl":"https://doi.org/10.1145/3351095.3372843","url":null,"abstract":"Word embeddings are a widely used set of natural language processing techniques that map words to vectors of real numbers. These vectors are used to improve the quality of generative and predictive models. Recent studies demonstrate that word embeddings contain and amplify biases present in data, such as stereotypes and prejudice. In this study, we provide a complete overview of bias in word embeddings. We develop a new technique for bias detection for gendered languages and use it to compare bias in embeddings trained on Wikipedia and on political social media data. We investigate bias diffusion and prove that existing biases are transferred to further machine learning models. We test two techniques for bias mitigation and show that the generally proposed methodology for debiasing models at the embeddings level is insufficient. Finally, we employ biased word embeddings and illustrate that they can be used for the detection of similar biases in new data. Given that word embeddings are widely used by commercial companies, we discuss the challenges and required actions towards fair algorithmic implementations and applications.","PeriodicalId":377829,"journal":{"name":"Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130688721","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"What to account for when accounting for algorithms: a systematic literature review on algorithmic accountability","authors":"M. Wieringa","doi":"10.1145/3351095.3372833","DOIUrl":"https://doi.org/10.1145/3351095.3372833","url":null,"abstract":"As research on algorithms and their impact proliferates, so do calls for scrutiny/accountability of algorithms. A systematic review of the work that has been done in the field of 'algorithmic accountability' has so far been lacking. This contribution puts forth such a systematic review, following the PRISMA statement. 242 English articles from the period 2008 up to and including 2018 were collected and extracted from Web of Science and SCOPUS, using a recursive query design coupled with computational methods. The 242 articles were prioritized and ordered using affinity mapping, resulting in 93 'core articles' which are presented in this contribution. The recursive search strategy made it possible to look beyond the term 'algorithmic accountability'. That is, the query also included terms closely connected to the theme (e.g. ethics and AI, regulation of algorithms). This approach allows for a perspective not just from critical algorithm studies, but an interdisciplinary overview drawing on material from data studies to law, and from computer science to governance studies. To structure the material, Bovens's widely accepted definition of accountability serves as a focal point. The material is analyzed on the five points Bovens identified as integral to accountability: its arguments on (1) the actor, (2) the forum, (3) the relationship between the two, (3) the content and criteria of the account, and finally (5) the consequences which may result from the account. The review makes three contributions. First, an integration of accountability theory in the algorithmic accountability discussion. Second, a cross-sectoral overview of the that same discussion viewed in light of accountability theory which pays extra attention to accountability risks in algorithmic systems. Lastly, it provides a definition of algorithmic accountability based on accountability theory and algorithmic accountability literature.","PeriodicalId":377829,"journal":{"name":"Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129292879","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}