A toolkit of dilemmas: Beyond debiasing and fairness formulas for responsible AI/ML

Approaches to fair and ethical AI have recently fell under the scrutiny of the emerging, chiefly qualitative, field of critical data studies, placing emphasis on the lack of sensitivity to context and complex social phenomena of such interventions. We employ some of these lessons to introduce a tripartite decision-making toolkit, informed by dilemmas encountered in the pursuit of responsible AI/ML. These are: (a) the opportunity dilemma between the availability of data shaping problem statements vs problem statements shaping data; (b) the trade-off between scalability and contextualizability (too much data versus too specific data); and (c) the epistemic positioning between the pragmatic technical objectivism and the reflexive relativism in acknowledging the social. This paper advocates for a situated reasoning and creative engagement with the dilemmas surrounding responsible algorithmic/data-driven systems, and going beyond the formulaic bias elimination and ethics operationalization narratives found in the fair-AI literature.Comment: 4 pages, 1 table. Accepted in IEEE International Symposium on Technology and Society 202

Fair and equitable AI in biomedical research and healthcare:Social science perspectives

Artificial intelligence (AI) offers opportunities but also challenges for biomedical research and healthcare. This position paper shares the results of the international conference “Fair medicine and AI” (online 3–5 March 2021). Scholars from science and technology studies (STS), gender studies, and ethics of science and technology formulated opportunities, challenges, and research and development desiderata for AI in healthcare. AI systems and solutions, which are being rapidly developed and applied, may have undesirable and unintended consequences including the risk of perpetuating health inequalities for marginalized groups. Socially robust development and implications of AI in healthcare require urgent investigation. There is a particular dearth of studies in human-AI interaction and how this may best be configured to dependably deliver safe, effective and equitable healthcare. To address these challenges, we need to establish diverse and interdisciplinary teams equipped to develop and apply medical AI in a fair, accountable and transparent manner. We formulate the importance of including social science perspectives in the development of intersectionally beneficent and equitable AI for biomedical research and healthcare, in part by strengthening AI health evaluation

Chronic p53-independent p21 expression causes genomic instability by deregulating replication licensing

The cyclin-dependent kinase inhibitor p21WAF1/CIP1 (p21) is a cell-cycle checkpoint effector and inducer of senescence, regulated by p53. Yet, evidence suggests that p21 could also be oncogenic, through a mechanism that has so far remained obscure. We report that a subset of atypical cancerous cells strongly expressing p21 showed proliferation features. This occurred predominantly in p53-mutant human cancers, suggesting p53-independent upregulation of p21 selectively in more aggressive tumour cells. Multifaceted phenotypic and genomic analyses of p21-inducible, p53-null, cancerous and near-normal cellular models showed that after an initial senescence-like phase, a subpopulation of p21-expressing proliferating cells emerged, featuring increased genomic instability, aggressiveness and chemoresistance. Mechanistically, sustained p21 accumulation inhibited mainly the CRL4–CDT2 ubiquitin ligase, leading to deregulated origin licensing and replication stress. Collectively, our data reveal the tumour-promoting ability of p21 through deregulation of DNA replication licensing machinery—an unorthodox role to be considered in cancer treatment, since p21 responds to various stimuli including some chemotherapy drugs

Induction of APOBEC3 exacerbates DNA replication stress and chromosomal instability in early breast and lung cancer evolution

APOBEC3 enzymes are cytosine deaminases implicated in cancer. Precisely when APOBEC3 expression is induced during cancer development remains to be defined. Here we show that specific APOBEC3 genes are upregulated in breast DCIS, and in pre-invasive lung cancer lesions coincident with cellular proliferation. We observe evidence of APOBEC3-mediated subclonal mutagenesis propagated from TRACERx pre-invasive to invasive NSCLC lesions. We find that APOBEC3B exacerbates DNA replication stress and chromosomal instability through incomplete replication of genomic DNA, manifested by accumulation of mitotic ultrafine bridges and 53BP1 nuclear bodies in the G1 phase of the cell cycle. Analysis of TRACERx NSCLC clinical samples and mouse lung cancer models, revealed APOBEC3B expression driving replication stress and chromosome missegregation. We propose that APOBEC3 is functionally implicated in the onset of chromosomal instability and somatic mutational heterogeneity in pre-invasive disease, providing fuel for selection early in cancer evolution

Artificial intelligence (AI): multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research and practice

As far back as the industrial revolution, great leaps in technical innovation succeeded in transforming numerous manual tasks and processes that had been in existence for decades where humans had reached the limits of physical capacity. Artificial Intelligence (AI) offers this same transformative potential for the augmentation and potential replacement of human tasks and activities within a wide range of industrial, intellectual and social applications. The pace of change for this new AI technological age is staggering, with new breakthroughs in algorithmic machine learning and autonomous decision making engendering new opportunities for continued innovation. The impact of AI is significant, with industries ranging from: finance, retail, healthcare, manufacturing, supply chain and logistics all set to be disrupted by the onset of AI technologies. The study brings together the collective insight from a number of leading expert contributors to highlight the significant opportunities, challenges and potential research agenda posed by the rapid emergence of AI within a number of domains: technological, business and management, science and technology, government and public sector. The research offers significant and timely insight to AI technology and its impact on the future of industry and society in general

A Toolkit of Dilemmas:Beyond debiasing and fairness formulas for responsible AI/ML

Approaches to fair and ethical AI have recently fell under the scrutiny of the emerging, chiefly qualitative, field of critical data studies, placing emphasis on the lack of sensitivity to context and complex social phenomena of such interventions. We employ some of these lessons to introduce a tripartite decision-making toolkit, informed by dilemmas encountered in the pursuit of responsible AI/ML. These are: (a) the opportunity dilemma between the availability of data shaping problem statements vs problem statements shaping data; (b) the trade-off between scalability and contextualizability (too much data versus too specific data); and (c) the epistemic positioning between the pragmatic technical objectivism and the reflexive relativism in acknowledging the social. This paper advocates for a situated reasoning and creative engagement with the dilemmas surrounding responsible algorithmic/data-driven systems, and going beyond the formulaic bias elimination and ethics operationalization narratives found in the fair-AI literature.Comment: 4 pages, 1 table. Accepted in IEEE International Symposium on Technology and Society 202

MKK7 and ARF: new players in the DNA damage response scenery.

Sensing, integrating, and processing of stressogenic signals must be followed by accurate differential response(s) for a cell to survive and avoid malignant transformation. The DNA damage response (DDR) pathway is vital in this process, as it deals with genotoxic/oncogenic insults, having p53 as a nodal effector that performs most of the above tasks. Accumulating data reveal that other pathways are also involved in the same or similar processes, conveying also to p53. Emerging questions are if, how, and when these additional pathways communicate with the DDR axis. Two such stress response pathways, involving the MKK7 stress-activated protein kinase (SAPK) and ARF, have been shown to be interlocked with the ATM/ATR-regulated DDR axis in a highly ordered manner. This creates a new landscape in the DDR orchestrated response to genotoxic/oncogenic insults that is currently discussed