Misplaced Trust: Measuring the Interference of Machine Learning in Human Decision-Making

ML decision-aid systems are increasingly common on the web, but their successful integration relies on people trusting them appropriately: they should use the system to fill in gaps in their ability, but recognize signals that the system might be incorrect. We measured how people's trust in ML recommendations differs by expertise and with more system information through a task-based study of 175 adults. We used two tasks that are difficult for humans: comparing large crowd sizes and identifying similar-looking animals. Our results provide three key insights: (1) People trust incorrect ML recommendations for tasks that they perform correctly the majority of the time, even if they have high prior knowledge about ML or are given information indicating the system is not confident in its prediction; (2) Four different types of system information all increased people's trust in recommendations; and (3) Math and logic skills may be as important as ML for decision-makers working with ML recommendations.Comment: 10 page

Privacy and Accountability in Black-Box Medicine

Black-box medicine—the use of big data and sophisticated machine learning techniques for health-care applications—could be the future of personalized medicine. Black-box medicine promises to make it easier to diagnose rare diseases and conditions, identify the most promising treatments, and allocate scarce resources among different patients. But to succeed, it must overcome two separate, but related, problems: patient privacy and algorithmic accountability. Privacy is a problem because researchers need access to huge amounts of patient health information to generate useful medical predictions. And accountability is a problem because black-box algorithms must be verified by outsiders to ensure they are accurate and unbiased, but this means giving outsiders access to this health information. This article examines the tension between the twin goals of privacy and accountability and develops a framework for balancing that tension. It proposes three pillars for an effective system of privacy-preserving accountability: substantive limitations on the collection, use, and disclosure of patient information; independent gatekeepers regulating information sharing between those developing and verifying black-box algorithms; and information-security requirements to prevent unintentional disclosures of patient information. The article examines and draws on a similar debate in the field of clinical trials, where disclosing information from past trials can lead to new treatments but also threatens patient privacy

POTs: Protective Optimization Technologies

Algorithmic fairness aims to address the economic, moral, social, and political impact that digital systems have on populations through solutions that can be applied by service providers. Fairness frameworks do so, in part, by mapping these problems to a narrow definition and assuming the service providers can be trusted to deploy countermeasures. Not surprisingly, these decisions limit fairness frameworks' ability to capture a variety of harms caused by systems. We characterize fairness limitations using concepts from requirements engineering and from social sciences. We show that the focus on algorithms' inputs and outputs misses harms that arise from systems interacting with the world; that the focus on bias and discrimination omits broader harms on populations and their environments; and that relying on service providers excludes scenarios where they are not cooperative or intentionally adversarial. We propose Protective Optimization Technologies (POTs). POTs provide means for affected parties to address the negative impacts of systems in the environment, expanding avenues for political contestation. POTs intervene from outside the system, do not require service providers to cooperate, and can serve to correct, shift, or expose harms that systems impose on populations and their environments. We illustrate the potential and limitations of POTs in two case studies: countering road congestion caused by traffic-beating applications, and recalibrating credit scoring for loan applicants.Comment: Appears in Conference on Fairness, Accountability, and Transparency (FAT* 2020). Bogdan Kulynych and Rebekah Overdorf contributed equally to this work. Version v1/v2 by Seda G\"urses, Rebekah Overdorf, and Ero Balsa was presented at HotPETS 2018 and at PiMLAI 201

The Market for User Data

Policymakers are today far more alert than ever before to the myriad ways in which tech companies collect and distribute consumers’ data with third-party data brokers and advertisers. We can attribute this new awareness to at least two major news stories from the past six or so years. The first came in 2013, when Edward Snowden, the former National Security Agency contractor, leaked highly classified materials that revealed the ways in which United States national security officials, with the indispensable cooperation of U.S. telecommunications companies, systematically monitored telephone conversations and electronic communications of U.S. citizens and foreign nationals. The story triggered a series of rebukes from civil rights groups, consumer advocates, and foreign leaders around the world. It is not clear whether or the extent to which the NSA or other government agencies have terminated those programs since Snowden’s revelation. The second came in early 2018, when another whistleblower revealed to journalists that researchers to whom Facebook had allowed to collect and study dozens of millions of users’ personal data, in turn, shared those troves of personal data with Cambridge Analytica, a political consultancy firm. Cambridge Analytica had promoted their access to this data to peddle “psychographic targeting” to political campaigns, including that of Donald Trump in 2016. This more recent revelation has exposed Facebook to what will likely be the largest fine imposed by the Federal Trade Commission (“FTC”) in history

Data analytics and algorithms in policing in England and Wales: Towards a new policy framework

RUSI was commissioned by the Centre for Data Ethics and Innovation (CDEI) to conduct an independent study into the use of data analytics by police forces in England and Wales, with a focus on algorithmic bias. The primary purpose of the project is to inform CDEI’s review of bias in algorithmic decision-making, which is focusing on four sectors, including policing, and working towards a draft framework for the ethical development and deployment of data analytics tools for policing. This paper focuses on advanced algorithms used by the police to derive insights, inform operational decision-making or make predictions. Biometric technology, including live facial recognition, DNA analysis and fingerprint matching, are outside the direct scope of this study, as are covert surveillance capabilities and digital forensics technology, such as mobile phone data extraction and computer forensics. However, because many of the policy issues discussed in this paper stem from general underlying data protection and human rights frameworks, these issues will also be relevant to other police technologies, and their use must be considered in parallel to the tools examined in this paper. The project involved engaging closely with senior police officers, government officials, academics, legal experts, regulatory and oversight bodies and civil society organisations. Sixty nine participants took part in the research in the form of semi-structured interviews, focus groups and roundtable discussions. The project has revealed widespread concern across the UK law enforcement community regarding the lack of official national guidance for the use of algorithms in policing, with respondents suggesting that this gap should be addressed as a matter of urgency. Any future policy framework should be principles-based and complement existing police guidance in a ‘tech-agnostic’ way. Rather than establishing prescriptive rules and standards for different data technologies, the framework should establish standardised processes to ensure that data analytics projects follow recommended routes for the empirical evaluation of algorithms within their operational context and evaluate the project against legal requirements and ethical standards. The new guidance should focus on ensuring multi-disciplinary legal, ethical and operational input from the outset of a police technology project; a standard process for model development, testing and evaluation; a clear focus on the human–machine interaction and the ultimate interventions a data driven process may inform; and ongoing tracking and mitigation of discrimination risk