Shubham Sharma, Yunfeng Zhang, J. Aliaga, Djallel Bouneffouf, Vinod Muthusamy, Kush R. Varshney
Machine learning models are prone to biased decisions due to biases in the datasets they are trained on. In this paper, we introduce a novel data augmentation technique to create a fairer dataset for model training that could also lend itself to understanding the type of bias existing in the dataset i.e. if bias arises from a lack of representation for a particular group (sampling bias) or if it arises because of human bias reflected in the labels (prejudice based bias). Given a dataset involving a protected attribute with a privileged and unprivileged group, we create an "ideal world'' dataset: for every data sample, we create a new sample having the same features (except the protected attribute(s)) and label as the original sample but with the opposite protected attribute value. The synthetic data points are sorted in order of their proximity to the original training distribution and added successively to the real dataset to create intermediate datasets. We theoretically show that two different notions of fairness: statistical parity difference (independence) and average odds difference (separation) always change in the same direction using such an augmentation. We also show submodularity of the proposed fairness-aware augmentation approach that enables an efficient greedy algorithm. We empirically study the effect of training models on the intermediate datasets and show that this technique reduces the two bias measures while keeping the accuracy nearly constant for three datasets. We then discuss the implications of this study on the disambiguation of sample bias and prejudice based bias and discuss how pre-processing techniques should be evaluated in general. The proposed method can be used by policy makers who want to use unbiased datasets to train machine learning models for their applications to add a subset of synthetic points to an extent that they are comfortable with to mitigate unwanted bias.
{"title":"Data Augmentation for Discrimination Prevention and Bias Disambiguation","authors":"Shubham Sharma, Yunfeng Zhang, J. Aliaga, Djallel Bouneffouf, Vinod Muthusamy, Kush R. Varshney","doi":"10.1145/3375627.3375865","DOIUrl":"https://doi.org/10.1145/3375627.3375865","url":null,"abstract":"Machine learning models are prone to biased decisions due to biases in the datasets they are trained on. In this paper, we introduce a novel data augmentation technique to create a fairer dataset for model training that could also lend itself to understanding the type of bias existing in the dataset i.e. if bias arises from a lack of representation for a particular group (sampling bias) or if it arises because of human bias reflected in the labels (prejudice based bias). Given a dataset involving a protected attribute with a privileged and unprivileged group, we create an \"ideal world'' dataset: for every data sample, we create a new sample having the same features (except the protected attribute(s)) and label as the original sample but with the opposite protected attribute value. The synthetic data points are sorted in order of their proximity to the original training distribution and added successively to the real dataset to create intermediate datasets. We theoretically show that two different notions of fairness: statistical parity difference (independence) and average odds difference (separation) always change in the same direction using such an augmentation. We also show submodularity of the proposed fairness-aware augmentation approach that enables an efficient greedy algorithm. We empirically study the effect of training models on the intermediate datasets and show that this technique reduces the two bias measures while keeping the accuracy nearly constant for three datasets. We then discuss the implications of this study on the disambiguation of sample bias and prejudice based bias and discuss how pre-processing techniques should be evaluated in general. The proposed method can be used by policy makers who want to use unbiased datasets to train machine learning models for their applications to add a subset of synthetic points to an extent that they are comfortable with to mitigate unwanted bias.","PeriodicalId":93612,"journal":{"name":"Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society","volume":"31 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81454433","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}
In this paper, we analyze the relation between data-related biases and practices of data annotation, by placing them in the context of market economy. We understand annotation as a praxis related to the sensemaking of data and investigate annotation practices for vision models by focusing on the values that are prioritized by industrial decision-makers and practitioners. The quality of data is critical for machine learning models as it holds the power to (mis-)represent the population it is intended to analyze. For autonomous systems to be able to make sense of the world, humans first need to make sense of the data these systems will be trained on. This paper addresses this issue, guided by the following research questions: Which goals are prioritized by decision-makers at the data annotation stage? How do these priorities correlate with data-related bias issues? Focusing on work practices and their context, our research goal aims at understanding the logics driving companies and their impact on the performed annotations. The study follows a qualitative design and is based on 24 interviews with relevant actors and extensive participatory observations, including several weeks of fieldwork at two companies dedicated to data annotation for vision models in Buenos Aires, Argentina and Sofia, Bulgaria. The prevalence of market-oriented values over socially responsible approaches is argued based on three corporate priorities that inform work practices in this field and directly shape the annotations performed: profit (short deadlines connected to the strive for profit are prioritized over alternative approaches that could prevent biased outcomes), standardization (the strive for standardized and, in many cases, reductive or biased annotations to make data fit the products and revenue plans of clients), and opacity (related to client's power to impose their criteria on the annotations that are performed. Criteria that most of the times remain opaque due to corporate confidentiality). Finally, we introduce three elements, aiming at developing ethics-oriented practices of data annotation, that could help prevent biased outcomes: transparency (regarding the documentation of data transformations, including information on responsibilities and criteria for decision-making.), education (training on the potential harms caused by AI and its ethical implications, that could help data annotators and related roles adopt a more critical approach towards the interpretation and labeling of data), and regulations (clear guidelines for ethical AI developed at the governmental level and applied both in private and public organizations).
{"title":"Biased Priorities, Biased Outcomes: Three Recommendations for Ethics-oriented Data Annotation Practices","authors":"Gunay Kazimzade, Milagros Miceli","doi":"10.1145/3375627.3375809","DOIUrl":"https://doi.org/10.1145/3375627.3375809","url":null,"abstract":"In this paper, we analyze the relation between data-related biases and practices of data annotation, by placing them in the context of market economy. We understand annotation as a praxis related to the sensemaking of data and investigate annotation practices for vision models by focusing on the values that are prioritized by industrial decision-makers and practitioners. The quality of data is critical for machine learning models as it holds the power to (mis-)represent the population it is intended to analyze. For autonomous systems to be able to make sense of the world, humans first need to make sense of the data these systems will be trained on. This paper addresses this issue, guided by the following research questions: Which goals are prioritized by decision-makers at the data annotation stage? How do these priorities correlate with data-related bias issues? Focusing on work practices and their context, our research goal aims at understanding the logics driving companies and their impact on the performed annotations. The study follows a qualitative design and is based on 24 interviews with relevant actors and extensive participatory observations, including several weeks of fieldwork at two companies dedicated to data annotation for vision models in Buenos Aires, Argentina and Sofia, Bulgaria. The prevalence of market-oriented values over socially responsible approaches is argued based on three corporate priorities that inform work practices in this field and directly shape the annotations performed: profit (short deadlines connected to the strive for profit are prioritized over alternative approaches that could prevent biased outcomes), standardization (the strive for standardized and, in many cases, reductive or biased annotations to make data fit the products and revenue plans of clients), and opacity (related to client's power to impose their criteria on the annotations that are performed. Criteria that most of the times remain opaque due to corporate confidentiality). Finally, we introduce three elements, aiming at developing ethics-oriented practices of data annotation, that could help prevent biased outcomes: transparency (regarding the documentation of data transformations, including information on responsibilities and criteria for decision-making.), education (training on the potential harms caused by AI and its ethical implications, that could help data annotators and related roles adopt a more critical approach towards the interpretation and labeling of data), and regulations (clear guidelines for ethical AI developed at the governmental level and applied both in private and public organizations).","PeriodicalId":93612,"journal":{"name":"Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society","volume":"449 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77854278","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}
Jeanna Neefe Matthews, G. Northup, Isabella Grasso, Stephen Lorenz, M. Babaeianjelodar, Hunter Bashaw, Sumona Mondal, Abigail V. Matthews, Mariama Njie, Jessica Goldthwaite
Software increasingly plays a key role in regulated areas like housing, hiring, and credit, as well as major public functions such as criminal justice and elections. It is easy for there to be unintended defects with a large impact on the lives of individuals and society as a whole. Preventing, finding, and fixing software defects is a key focus of both industrial software development efforts as well as academic research in software engineering. In this paper, we discuss flaws in the larger socio-technical decision-making processes in which critical black-box software systems are developed, deployed, and trusted. We use criminal justice software, specifically probabilistic genotyping (PG) software, as a concrete example. We describe how PG software systems, designed to do the same job, produce different results. We highlight the under-appreciated impact of changes in key parameters and the disparate impact that one such parameter can have on different racial/ethnic groups. We propose concrete changes to the socio-technical decision-making processes surrounding the use of PG software that could be used to incentivize iterative improvements in the accuracy, fairness, reliability, and accountability of these systems.
{"title":"When Trusted Black Boxes Don't Agree: Incentivizing Iterative Improvement and Accountability in Critical Software Systems","authors":"Jeanna Neefe Matthews, G. Northup, Isabella Grasso, Stephen Lorenz, M. Babaeianjelodar, Hunter Bashaw, Sumona Mondal, Abigail V. Matthews, Mariama Njie, Jessica Goldthwaite","doi":"10.1145/3375627.3375807","DOIUrl":"https://doi.org/10.1145/3375627.3375807","url":null,"abstract":"Software increasingly plays a key role in regulated areas like housing, hiring, and credit, as well as major public functions such as criminal justice and elections. It is easy for there to be unintended defects with a large impact on the lives of individuals and society as a whole. Preventing, finding, and fixing software defects is a key focus of both industrial software development efforts as well as academic research in software engineering. In this paper, we discuss flaws in the larger socio-technical decision-making processes in which critical black-box software systems are developed, deployed, and trusted. We use criminal justice software, specifically probabilistic genotyping (PG) software, as a concrete example. We describe how PG software systems, designed to do the same job, produce different results. We highlight the under-appreciated impact of changes in key parameters and the disparate impact that one such parameter can have on different racial/ethnic groups. We propose concrete changes to the socio-technical decision-making processes surrounding the use of PG software that could be used to incentivize iterative improvements in the accuracy, fairness, reliability, and accountability of these systems.","PeriodicalId":93612,"journal":{"name":"Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society","volume":"2 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88698725","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}
Margaret Mitchell, Dylan Baker, Nyalleng Moorosi, Emily L. Denton, B. Hutchinson, A. Hanna, Timnit Gebru, Jamie Morgenstern
The ethical concept of fairness has recently been applied in machine learning (ML) settings to describe a wide range of constraints and objectives. When considering the relevance of ethical concepts to subset selection problems, the concepts of diversity and inclusion are additionally applicable in order to create outputs that account for social power and access differentials. We introduce metrics based on these concepts, which can be applied together, separately, and in tandem with additional fairness constraints. Results from human subject experiments lend support to the proposed criteria. Social choice methods can additionally be leveraged to aggregate and choose preferable sets, and we detail how these may be applied.
{"title":"Diversity and Inclusion Metrics in Subset Selection","authors":"Margaret Mitchell, Dylan Baker, Nyalleng Moorosi, Emily L. Denton, B. Hutchinson, A. Hanna, Timnit Gebru, Jamie Morgenstern","doi":"10.1145/3375627.3375832","DOIUrl":"https://doi.org/10.1145/3375627.3375832","url":null,"abstract":"The ethical concept of fairness has recently been applied in machine learning (ML) settings to describe a wide range of constraints and objectives. When considering the relevance of ethical concepts to subset selection problems, the concepts of diversity and inclusion are additionally applicable in order to create outputs that account for social power and access differentials. We introduce metrics based on these concepts, which can be applied together, separately, and in tandem with additional fairness constraints. Results from human subject experiments lend support to the proposed criteria. Social choice methods can additionally be leveraged to aggregate and choose preferable sets, and we detail how these may be applied.","PeriodicalId":93612,"journal":{"name":"Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society","volume":"24 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89237995","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}
As 'control' is increasingly ceded to AI systems, potentially Artificial General Intelligence (AGI) humanity may be facing an identity crisis sooner rather than later, whereby the notion of 'intelligence' no longer remains solely our own. This paper characterizes the problem in terms of an impending loss of control and proposes a relational shift in our attitude towards AI. The shortcomings of value alignment as a solution to the problem are outlined which necessitate an extension of these principles. One such approach is considering strongly relational Indigenous epistemologies. The value of Indigenous perspectives has not been canvassed widely in the literature. Their utility becomes clear when considering the existence of well-developed epistemologies adept at accounting for the non-human, a task that defies Western anthropocentrism. Accommodating AI by considering it as part of our network is a step towards building a symbiotic relationship. Given that AGI questions our fundamental notions of what it means to have human rights, it is argued that in order to co-exist, we find assistance in Indigenous traditions such as the Hawaiian and Lakota ontologies. Lakota rituals provide comfort with the conception of non-human soul-bearer while Hawaiian stories provide possible relational schema to frame our relationship with AI.
{"title":"Artificial Intelligence and Indigenous Perspectives: Protecting and Empowering Intelligent Human Beings","authors":"Suvradip Maitra","doi":"10.1145/3375627.3375845","DOIUrl":"https://doi.org/10.1145/3375627.3375845","url":null,"abstract":"As 'control' is increasingly ceded to AI systems, potentially Artificial General Intelligence (AGI) humanity may be facing an identity crisis sooner rather than later, whereby the notion of 'intelligence' no longer remains solely our own. This paper characterizes the problem in terms of an impending loss of control and proposes a relational shift in our attitude towards AI. The shortcomings of value alignment as a solution to the problem are outlined which necessitate an extension of these principles. One such approach is considering strongly relational Indigenous epistemologies. The value of Indigenous perspectives has not been canvassed widely in the literature. Their utility becomes clear when considering the existence of well-developed epistemologies adept at accounting for the non-human, a task that defies Western anthropocentrism. Accommodating AI by considering it as part of our network is a step towards building a symbiotic relationship. Given that AGI questions our fundamental notions of what it means to have human rights, it is argued that in order to co-exist, we find assistance in Indigenous traditions such as the Hawaiian and Lakota ontologies. Lakota rituals provide comfort with the conception of non-human soul-bearer while Hawaiian stories provide possible relational schema to frame our relationship with AI.","PeriodicalId":93612,"journal":{"name":"Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society","volume":"128 1-2 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73877798","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}
This paper argues that the concept of intelligence is highly value-laden in ways that impact on the field of AI and debates about its risks and opportunities. This value-ladenness stems from the historical use of the concept of intelligence in the legitimation of dominance hierarchies. The paper first provides a brief overview of the history of this usage, looking at the role of intelligence in patriarchy, the logic of colonialism and scientific racism. It then highlights five ways in which this ideological legacy might be interacting with debates about AI and its risks and opportunities: 1) how some aspects of the AI debate perpetuate the fetishization of intelligence; 2) how the fetishization of intelligence impacts on diversity in the technology industry; 3) how certain hopes for AI perpetuate notions of technology and the mastery of nature; 4) how the association of intelligence with the professional class misdirects concerns about AI; and 5) how the equation of intelligence and dominance fosters fears of superintelligence. This paper therefore takes a first step in bringing together the literature on intelligence testing, eugenics and colonialism from a range of disciplines with that on the ethics and societal impact of AI.
{"title":"The Problem with Intelligence: Its Value-Laden History and the Future of AI","authors":"S. Cave","doi":"10.1145/3375627.3375813","DOIUrl":"https://doi.org/10.1145/3375627.3375813","url":null,"abstract":"This paper argues that the concept of intelligence is highly value-laden in ways that impact on the field of AI and debates about its risks and opportunities. This value-ladenness stems from the historical use of the concept of intelligence in the legitimation of dominance hierarchies. The paper first provides a brief overview of the history of this usage, looking at the role of intelligence in patriarchy, the logic of colonialism and scientific racism. It then highlights five ways in which this ideological legacy might be interacting with debates about AI and its risks and opportunities: 1) how some aspects of the AI debate perpetuate the fetishization of intelligence; 2) how the fetishization of intelligence impacts on diversity in the technology industry; 3) how certain hopes for AI perpetuate notions of technology and the mastery of nature; 4) how the association of intelligence with the professional class misdirects concerns about AI; and 5) how the equation of intelligence and dominance fosters fears of superintelligence. This paper therefore takes a first step in bringing together the literature on intelligence testing, eugenics and colonialism from a range of disciplines with that on the ethics and societal impact of AI.","PeriodicalId":93612,"journal":{"name":"Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society","volume":"19 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72583819","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}
Even as public pressure mounts for technology companies to consider societal impacts of products, industries and governments in the AI race are demanding technical talent. To meet this demand, universities clamor to add technical artificial intelligence (AI) and machine learning (ML) courses into computing curriculum-but how are societal and ethical considerations part of this landscape? We explore two pathways for ethics content in AI education: (1) standalone AI ethics courses, and (2) integrating ethics into technical AI courses. For both pathways, we ask: What is being taught? As we train computer scientists who will build and deploy AI tools, how are we training them to consider the consequences of their work? In this exploratory work, we qualitatively analyzed 31 standalone AI ethics classes from 22 U.S. universities and 20 AI/ML technical courses from 12 U.S. universities to understand which ethics-related topics instructors include in courses. We identify and categorize topics in AI ethics education, share notable practices, and note omissions. Our analysis will help AI educators identify what topics should be taught and create scaffolding for developing future AI ethics education.
{"title":"More Than \"If Time Allows\": The Role of Ethics in AI Education","authors":"Natalie Garrett, Nathan Beard, Casey Fiesler","doi":"10.1145/3375627.3375868","DOIUrl":"https://doi.org/10.1145/3375627.3375868","url":null,"abstract":"Even as public pressure mounts for technology companies to consider societal impacts of products, industries and governments in the AI race are demanding technical talent. To meet this demand, universities clamor to add technical artificial intelligence (AI) and machine learning (ML) courses into computing curriculum-but how are societal and ethical considerations part of this landscape? We explore two pathways for ethics content in AI education: (1) standalone AI ethics courses, and (2) integrating ethics into technical AI courses. For both pathways, we ask: What is being taught? As we train computer scientists who will build and deploy AI tools, how are we training them to consider the consequences of their work? In this exploratory work, we qualitatively analyzed 31 standalone AI ethics classes from 22 U.S. universities and 20 AI/ML technical courses from 12 U.S. universities to understand which ethics-related topics instructors include in courses. We identify and categorize topics in AI ethics education, share notable practices, and note omissions. Our analysis will help AI educators identify what topics should be taught and create scaffolding for developing future AI ethics education.","PeriodicalId":93612,"journal":{"name":"Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society","volume":"90 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75455921","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}
Recent advances in artificial intelligence applications have sparked scholarly and public attention to the challenges of the ethical design of technologies. These conversations about ethics have been targeted largely at technology designers and concerned with helping to inform building better and fairer AI tools and technologies. This approach, however, addresses only a small part of the problem of responsible use and will not be adequate for describing or redressing the problems that will arise as more types of AI technologies are more widely used. Many of the tools being developed today have potentially enormous and historic impacts on how people work, how society organises, stores and distributes information, where and how people interact with one another, and how people's work is valued and compensated. And yet, our ethical attention has looked at a fairly narrow range of questions about expanding the access to, fairness of, and accountability for existing tools. Instead, I argue that scholars should develop much broader questions of about the reconfiguration of societal power, for which AI technologies form a crucial component. This talk will argue that AI ethics needs to expand its theoretical and methodological toolkit in order to move away from prioritizing notions of good design that privilege the work of good and ethical technology designers. Instead, using approaches from feminist theory, organization studies, and science and technology, I argue for expanding how we evaluate uses of AI. This approach begins with the assumption of socially informed technological affordances, or "imagined affordances" [1] shaping how people understand and use technologies in practice. It also gives centrality to the power of social institutions for shaping technologies-in-practice.
{"title":"From Bad Users and Failed Uses to Responsible Technologies: A Call to Expand the AI Ethics Toolkit","authors":"Gina Neff","doi":"10.1145/3375627.3377141","DOIUrl":"https://doi.org/10.1145/3375627.3377141","url":null,"abstract":"Recent advances in artificial intelligence applications have sparked scholarly and public attention to the challenges of the ethical design of technologies. These conversations about ethics have been targeted largely at technology designers and concerned with helping to inform building better and fairer AI tools and technologies. This approach, however, addresses only a small part of the problem of responsible use and will not be adequate for describing or redressing the problems that will arise as more types of AI technologies are more widely used. Many of the tools being developed today have potentially enormous and historic impacts on how people work, how society organises, stores and distributes information, where and how people interact with one another, and how people's work is valued and compensated. And yet, our ethical attention has looked at a fairly narrow range of questions about expanding the access to, fairness of, and accountability for existing tools. Instead, I argue that scholars should develop much broader questions of about the reconfiguration of societal power, for which AI technologies form a crucial component. This talk will argue that AI ethics needs to expand its theoretical and methodological toolkit in order to move away from prioritizing notions of good design that privilege the work of good and ethical technology designers. Instead, using approaches from feminist theory, organization studies, and science and technology, I argue for expanding how we evaluate uses of AI. This approach begins with the assumption of socially informed technological affordances, or \"imagined affordances\" [1] shaping how people understand and use technologies in practice. It also gives centrality to the power of social institutions for shaping technologies-in-practice.","PeriodicalId":93612,"journal":{"name":"Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society","volume":"23 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90240671","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}
The widespread deployment of machine learning models in high- stakes decision making scenarios requires a code of ethics for machine learning practitioners. We identify four of the primary components required for the ethical practice of machine learn- ing: transparency, fairness, accountability, and reproducibility. We introduce Arbiter, a domain-specific programming language for machine learning practitioners that is designed for ethical machine learning. Arbiter provides a notation for recording how machine learning models will be trained, and we show how this notation can encourage the four described components of ethical machine learning.
{"title":"Arbiter","authors":"Julian Zucker, Myraeka d'Leeuwen","doi":"10.1145/3375627.3375858","DOIUrl":"https://doi.org/10.1145/3375627.3375858","url":null,"abstract":"The widespread deployment of machine learning models in high- stakes decision making scenarios requires a code of ethics for machine learning practitioners. We identify four of the primary components required for the ethical practice of machine learn- ing: transparency, fairness, accountability, and reproducibility. We introduce Arbiter, a domain-specific programming language for machine learning practitioners that is designed for ethical machine learning. Arbiter provides a notation for recording how machine learning models will be trained, and we show how this notation can encourage the four described components of ethical machine learning.","PeriodicalId":93612,"journal":{"name":"Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society","volume":"111 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79315325","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}
Daniel Karpati, A. Najjar, Diego Agustín Ambrossio
The recent unprecedented popularity of food recommender applications has raised several issues related to the ethical, societal and legal implications of relying on these applications. In this paper, in order to assess the relevant ethical issues, we rely on the emerging principles across the AI & Ethics community and define them tailored context specifically. Considering the popular Food Recommender Systems (henceforth F-RS) in the European market cannot be regarded as personalised F-RS, we show how merely this lack of feature shifts the relevance of the focal ethical concerns. We identify the major challenges and propose a scheme for how explicit ethical agendas should be explained. We also argue how a multi-stakeholder approach is indispensable to ensure producing long-term benefits for all stakeholders. After proposing eight ethical desiderata points for F-RS, we present a case-study and assess it based on our proposed desiderata points.
{"title":"Ethics of Food Recommender Applications","authors":"Daniel Karpati, A. Najjar, Diego Agustín Ambrossio","doi":"10.1145/3375627.3375874","DOIUrl":"https://doi.org/10.1145/3375627.3375874","url":null,"abstract":"The recent unprecedented popularity of food recommender applications has raised several issues related to the ethical, societal and legal implications of relying on these applications. In this paper, in order to assess the relevant ethical issues, we rely on the emerging principles across the AI & Ethics community and define them tailored context specifically. Considering the popular Food Recommender Systems (henceforth F-RS) in the European market cannot be regarded as personalised F-RS, we show how merely this lack of feature shifts the relevance of the focal ethical concerns. We identify the major challenges and propose a scheme for how explicit ethical agendas should be explained. We also argue how a multi-stakeholder approach is indispensable to ensure producing long-term benefits for all stakeholders. After proposing eight ethical desiderata points for F-RS, we present a case-study and assess it based on our proposed desiderata points.","PeriodicalId":93612,"journal":{"name":"Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society","volume":"10 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84318140","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}
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