From plane crashes to algorithmic harm: applicability of safety engineering frameworks for responsible ML

Inappropriate design and deployment of machine learning (ML) systems leads to negative downstream social and ethical impact -- described here as social and ethical risks -- for users, society and the environment. Despite the growing need to regulate ML systems, current processes for assessing and mitigating risks are disjointed and inconsistent. We interviewed 30 industry practitioners on their current social and ethical risk management practices, and collected their first reactions on adapting safety engineering frameworks into their practice -- namely, System Theoretic Process Analysis (STPA) and Failure Mode and Effects Analysis (FMEA). Our findings suggest STPA/FMEA can provide appropriate structure toward social and ethical risk assessment and mitigation processes. However, we also find nontrivial challenges in integrating such frameworks in the fast-paced culture of the ML industry. We call on the ML research community to strengthen existing frameworks and assess their efficacy, ensuring that ML systems are safer for all people

Beyond Window Dressing: Public Participation for Marginalized Communities in the Datafied Society

We live in a datafied society in which our personal data is being constantly harvested, analyzed, and sold by public and private entities, and yet we have little control over our data and little voice in how it is used. In light of the impacts of algorithmic decision-making systems—including those that run on machine learning and artificial intelligence—there are increasing calls to integrate public participation into the adoption, design, and oversight of these tech tools. Stakeholder input is particularly crucial for members of marginalized groups, who bear the disproportionate harms of data-centric technologies. Yet, recent calls for public participation have been mostly hortatory and without specific strategies or realistic recommendations. As this Article explains, policy makers need not operate from a blank slate. For decades, a variety of American statutory regimes have mandated public participation, such as in the areas of environmental law, land use law, and anti-poverty programs. Such mandates have had outsized effects on communities suffering from economic disadvantage and racial and ethnic discrimination. This Article contends that we should examine these regulatory mandates in thinking about how to include the perspectives of marginalized stakeholders in the datafied society. The core takeaway is that meaningful public participation is extremely challenging and does not happen without intentional and inclusive design. At its best, public input can improve outputs and empower stakeholders. At its worst, it operates as a form of “window dressing,” in which marginalized communities have no real power to effect outcomes, thus generating distrust and alienation. Case studies show that meaningful public participation is most likely to result when there are hard-law requirements for public participation and when decision-makers operate transparently and recognize the value of the public’s expertise. In addition, impacted communities must be provided with capacity-building tools and resources to support their engagement. As legislative proposals to enhance tech accountability—through algorithmic impact assessments, audits, and other tools—gain steam, we must heed these lessons

Beyond Window Dressing: Public Participation for Marginalized Communities in the Datafied Society

We live in a datafied society in which our personal data is being constantly harvested, analyzed, and sold by public and private entities, and yet we have little control over our data and little voice in how it is used. In light of the impacts of algorithmic decision-making systems—including those that run on machine learning and artificial intelligence—there are increasing calls to integrate public participation into the adoption, design, and oversight of these tech tools. Stakeholder input is particularly crucial for members of marginalized groups, who bear the disproportionate harms of data-centric technologies. Yet, recent calls for public participation have been mostly hortatory and without specific strategies or realistic recommendations. As this Article explains, policy makers need not operate from a blank slate. For decades, a variety of American statutory regimes have mandated public participation, such as in the areas of environmental law, land use law, and anti-poverty programs. Such mandates have had outsized effects on communities suffering from economic disadvantage and racial and ethnic discrimination. This Article contends that we should examine these regulatory mandates in thinking about how to include the perspectives of marginalized stakeholders in the datafied society. The core takeaway is that meaningful public participation is extremely challenging and does not happen without intentional and inclusive design. At its best, public input can improve outputs and empower stakeholders. At its worst, it operates as a form of “window dressing,” in which marginalized communities have no real power to effect outcomes, thus generating distrust and alienation. Case studies show that meaningful public participation is most likely to result when there are hard-law requirements for public participation and when decision-makers operate transparently and recognize the value of the public’s expertise. In addition, impacted communities must be provided with capacity-building tools and resources to support their engagement. As legislative proposals to enhance tech accountability—through algorithmic impact assessments, audits, and other tools—gain steam, we must heed these lessons

Pitfalls and Tensions in Digitalizing Talent Acquisition: An Analysis of HRM Professionals’ Considerations Related to Digital Ethics

The practices of organizational talent acquisition are rapidly transforming as a result of the proliferation of information systems that support decision-making, ranging from applicant tracking systems to recruitment chatbots. As part of human resource management (HRM), talent acquisition covers recruitment and team-assembly activities and is allegedly in dire need for digital aid. We analyze the pitfalls and tensions of digitalization in this area through a lens that builds on the interdisciplinary literature related to digital ethics. Using three relevant landmark papers, we analyzed qualitative data from 47 interviews of HRM professionals in Finland, including team-assembly facilitators and recruitment experts. The analysis highlights 14 potential tensions and pitfalls, such as the tension between requesting detailed data versus respecting privacy and the pitfall of unequal treatment across application channels. We identify that the values of autonomy, fairness and utility are often especially at risk of being compromised. We discuss the tendency of the binary considerations related to human and automated decision making, and the reasons for the incompatibility between current digital systems and organizations’ needs for talent acquisition.Peer reviewe

Internet of Things. Information Processing in an Increasingly Connected World

This open access book constitutes the refereed post-conference proceedings of the First IFIP International Cross-Domain Conference on Internet of Things, IFIPIoT 2018, held at the 24th IFIP World Computer Congress, WCC 2018, in Poznan, Poland, in September 2018. The 12 full papers presented were carefully reviewed and selected from 24 submissions. Also included in this volume are 4 WCC 2018 plenary contributions, an invited talk and a position paper from the IFIP domain committee on IoT. The papers cover a wide range of topics from a technology to a business perspective and include among others hardware, software and management aspects, process innovation, privacy, power consumption, architecture, applications

Everyday Automation

This Open Access book brings the experiences of automation as part of quotidian life into focus. It asks how, where and when automated technologies and systems are emerging in everyday life across different global regions? What are their likely impacts in the present and future? How do engineers, policy makers, industry stakeholders and designers envisage artificial intelligence (AI) and automated decision-making (ADM) as solutions to individual and societal problems? How do these future visions compare with the everyday realities, power relations and social inequalities in which AI and ADM are experienced? What do people know about automation and what are their experiences of engaging with ‘actually existing’ AI and ADM technologies? An international team of leading scholars bring together research developed across anthropology, sociology, media and communication studies and ethnology, which shows how by rehumanising automation, we can gain deeper understandings of its societal impacts