The moral authority of ethics codes stems from an assumption that they serve a unified society, yet this ignores the political aspects of any shared resource. The sociologist Howard S. Becker challenged researchers to clarify their power and responsibility in the classic essay: Whose Side Are We On. Building on Becker's hierarchy of credibility, we report on a critical discourse analysis of data ethics codes and emerging conceptualizations of beneficence, or the "social good", of data technology. The analysis revealed that ethics codes from corporations and professional associations conflated consumers with society and were largely silent on agency. Interviews with community organizers about social change in the digital era supplement the analysis, surfacing the limits of technical solutions to concerns of marginalized communities. Given evidence that highlights the gulf between the documents and lived experiences, we argue that ethics codes that elevate consumers may simultaneously subordinate the needs of vulnerable populations. Understanding contested digital resources is central to the emerging field of public interest technology. We introduce the concept of digital differential vulnerability to explain disproportionate exposures to harm within data technology and suggest recommendations for future ethics codes.
While we have witnessed a rapid growth of ethics documents meant to guide AI development, the promotion of AI ethics has nonetheless proceeded with little input from AI practitioners themselves. Given the proliferation of AI for Social Good initiatives, this is an emerging gap that needs to be addressed in order to develop more meaningful ethical approaches to AI use and development. This paper offers a methodology, a shared fairness approach, aimed at identifying the needs of AI practitioners when it comes to confronting and resolving ethical challenges and to find a third space where their o…
There is a growing need for data-driven research efforts on how the public perceives the ethical, moral, and legal issues of autonomous AI systems. The current debate on the responsibility gap posed by these systems is one such example. This work proposes a mixed AI ethics model that allows normative and descriptive research to complement each other, by aiding scholarly discussion with data gathered from the public. We discuss its implications on bridging the gap between optimistic and pessimistic views towards AI systems' deployment.
The ubiquitous presence of software in the products we use, together with Artificial Intelligence in these products, has led to an increasing need for consumer trust. Consumers often lose faith in products, and the lack of Trust propagates to the companies behind them. This is even more so in mission-critical systems such as autonomous vehicles and clinical support systems. This paper follows grounded theory principles to elicit knowledge related to Trust, Ethics, and Transparency. We approach these qualities as Non-Functional Requirements (NFRs), aiming to build catalogs to subsidize the cons…
Institute of Data Science, Maastricht University（马斯特里赫特大学数据科学研究所）
Data science is an interdisciplinary research area where scientists are typically working with data coming from different fields. When using and analyzing data, the scientists implicitly agree to follow standards, procedures, and rules set in these fields. However, guidance on the responsibilities of the data scientists and the other involved actors in a data science project is typically missing. While literature shows that novel frameworks and tools are being proposed in support of open-science, data reuse, and research data management, there are currently no frameworks that can fully express…
A Detailed Study on LLM Biases Concerning Corporate Social Responsibility and Green Supply Chains
Research Center Trustworthy Data Science and Security, University Alliance Ruhr（可信数据科学与安全研究所以及鲁尔大学联盟）；Department of Computer Science, University of Duisburg-Essen（杜伊斯堡- Essen大学计算机科学系）；Department of Computer Science, Technical University Dortmund（图恩大学计算机科学系）；Chair of Mathematical Statistics and Applications in Industry, Technical University Dortmund（工业数学统计与应用教授职位，图恩大学）
Cytel Inc（赛特尔公司）；Helmut-Schmidt-Universität, Universität der Bundeswehr Hamburg（赫尔穆特·施密特大学（汉堡联邦国防军大学））
The goal of this article is to inspire data scientists to participate in the debate on the impact that their professional work has on society, and to become active in public debates on the digital world as data science professionals. How do ethical principles (e.g., fairness, justice, beneficence, and non-maleficence) relate to our professional lives? What lies in our responsibility as professionals by our expertise in the field? More specifically this article makes an appeal to statisticians to join that debate, and to be part of the community that establishes data science as a proper profess…
Commercial AI, Conflict, and Moral Responsibility: A theoretical analysis and practical approach to the moral responsibilities associated with dual-use AI technology
University of California, San Diego（加利福尼亚大学圣地亚哥分校）
These past few months have been especially challenging, and the deployment of technology in ways hitherto untested at an unrivalled pace has left the internet and technology watchers aghast. Artificial intelligence has become the byword for technological progress and is being used in everything from helping us combat the COVID-19 pandemic to nudging our attention in different directions as we all spend increasingly larger amounts of time online. It has never been more important that we keep a sharp eye out on the development of this field and how it is shaping our society and interactions with…
Artificial intelligence (AI) research is routinely criticized for its real and potential impacts on society, and we lack adequate institutional responses to this criticism and to the responsibility that it reflects. AI research often falls outside the purview of existing feedback mechanisms such as the Institutional Review Board (IRB), which are designed to evaluate harms to human subjects rather than harms to human society. In response, we have developed the Ethics and Society Review board (ESR), a feedback panel that works with researchers to mitigate negative ethical and societal aspects of…
Ask before you Build: Rethinking AI-for-Good in Human Trafficking Interventions
McGill University, Mila-Quebec AI Institute（麦吉尔大学，魁北克AI研究所）；McGill University（麦吉尔大学）；Universite de Montreal, Mila-Quebec AI Institute（蒙特利尔大学，魁北克AI研究所）；Mila-Quebec AI Institute（魁北克AI研究所）
The data revolution continues to transform every sector of science, industry and government. Due to the incredible impact of data-driven technology on society, we are becoming increasingly aware of the imperative to use data and algorithms responsibly -- in accordance with laws and ethical norms. In this article we discuss three recent regulatory frameworks: the European Union's General Data Protection Regulation (GDPR), the New York City Automated Decisions Systems (ADS) Law, and the Net Neutrality principle, that aim to protect the rights of individuals who are impacted by data collection an…
When we use machine learning for public policy, we find that many useful variables are associated with others on which it would be ethically problematic to base decisions. This problem becomes particularly acute in the Big Data era, when predictions are often made in the absence of strong theories for underlying causal mechanisms. We describe the dangers to democratic decision-making when high-performance algorithms fail to provide an explicit account of causation. We then demonstrate how information theory allows us to degrade predictions so that they decorrelate from protected variables with…
With the exponential advancement of business technology in recent years, data-driven decision making has become the core of most industries. With the rise of new privacy regulations such as the General Data Protection Regulation in the European Union and the California Consumer Privacy Act in the United States, companies dealing with personal data had to conform to these changes and adapt their processes accordingly. This obviously included the transportation industry with their use of location data. At the other side of the spectrum, users still expect a form of personalization, without havin…
