Emotional Biosensing: Exploring Critical Alternatives

Emotional biosensing is rising in daily life: Data and categories claim to know how people feel and suggest what they should do about it, while CSCW explores new biosensing possibilities. Prevalent approaches to emotional biosensing are too limited, focusing on the individual, optimization, and normative categorization. Conceptual shifts can help explore alternatives: toward materiality, from representation toward performativity, inter-action to intra-action, shifting biopolitics, and shifting affect/desire. We contribute (1) synthesizing wide-ranging conceptual lenses, providing analysis connecting them to emotional biosensing design, (2) analyzing selected design exemplars to apply these lenses to design research, and (3) offering our own recommendations for designers and design researchers. In particular we suggest humility in knowledge claims with emotional biosensing, prioritizing care and affirmation over self- improvement, and exploring alternative desires. We call for critically questioning and generatively re- imagining the role of data in configuring sensing, feeling, ‘the good life,’ and everyday experience

Algorithmic Transparency: Concepts, Antecedents, and Consequences – A Review and Research Framework

The widespread and growing use of algorithm-enabled technologies across many aspects of public and private life is increasingly sparking concerns about the lack of transparency regarding the inner workings of algorithms. This has led to calls for (more) algorithmic transparency (AT), which refers to the disclosure of information about algorithms to enable understanding, critical review, and adjustment. To set the stage for future research on AT, our study draws on previous work to provide a more nuanced conceptualization of AT, including the explicit distinction between AT as action and AT as perception. On this conceptual basis, we set forth to conduct a comprehensive and systematic review of the literature on AT antecedents and consequences. Subsequently, we develop an integrative framework to organize the existing literature and guide future work. Our framework consists of seven central relationships: (1) AT as action versus AT as perception; factors (2) triggering and (3) shaping AT as action; (4) factors shaping AT as perception; as well as AT as perception leading to (5) rational-cognitive and (6) affective-emotional responses, and to (7) (un-)intended behavioral effects. Building on the review insights, we identify and discuss notable research gaps and inconsistencies, along with resulting opportunities for future research

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

Fairness Perceptions of Algorithmic Decision-Making: A Systematic Review of the Empirical Literature

Algorithmic decision-making (ADM) increasingly shapes people's daily lives. Given that such autonomous systems can cause severe harm to individuals and social groups, fairness concerns have arisen. A human-centric approach demanded by scholars and policymakers requires taking people's fairness perceptions into account when designing and implementing ADM. We provide a comprehensive, systematic literature review synthesizing the existing empirical insights on perceptions of algorithmic fairness from 39 empirical studies spanning multiple domains and scientific disciplines. Through thorough coding, we systemize the current empirical literature along four dimensions: (a) algorithmic predictors, (b) human predictors, (c) comparative effects (human decision-making vs. algorithmic decision-making), and (d) consequences of ADM. While we identify much heterogeneity around the theoretical concepts and empirical measurements of algorithmic fairness, the insights come almost exclusively from Western-democratic contexts. By advocating for more interdisciplinary research adopting a society-in-the-loop framework, we hope our work will contribute to fairer and more responsible ADM