Abhishek Mishra

Healthcare professionals have often been acclimatised as one of the most challenging (and promising as well) when it comes to the management of people at workplaces. Strange but true, the aspects of employment relations at workplaces have received little research consideration in healthcare sector even in the developed countries. India, becoming one of the most attractive destinations for medical tourism, has given substantial impetus to the healthcare sector which gave birth to new forms of employment relations practices in the sector. Adopting a qualitative approach where literature research along with the use of search engine using selected key words, the authors in this chapter intend to identify the emerging paradigms and new directions of employment relations in the sector. The objectives in this chapter will also centre on mapping the future trajectory of employment relations in the healthcare sector with specific considerations for medical tourism in India.

This paper addresses how to act towards digital agents while uncertain about their moral status. It focuses specifically on the problem of how to act towards simulated minds operated by an artificial superintelligence. This problem can be treated as a sub-set of the larger problems of AI-safety and also invokes debates about the grounds of moral status. The paper presents a formal structure for solving the problem by first constraining it as a sub-problem to the AI-safety problem, and then suggesting a decision-theoretic approach to how this problem can be solved under uncertainty about what the true grounds of moral status are, and whether such simulations do possess these relevant grounds. The paper ends by briefly suggesting a way to generalize the approach.

Many are calling for concrete mechanisms of oversight for health research involving artificial intelligence (AI). In response, institutional review boards (IRBs) are being turned to as a familiar model of governance. Here, we examine the IRB model as a form of ethics oversight for health research that uses AI. We consider the model's origins, analyze the challenges IRBs are facing in the contexts of both industry and academia, and offer concrete recommendations for how these committees might be adapted in order to provide an effective mechanism of oversight for health‐related AI research.

Durán et al argue in ‘Who is afraid of black box algorithms? On the epistemological and ethical basis of trust in medical AI’1 that traditionally proposed solutions to make black box machine learning models in medicine less opaque and more transparent are, though necessary, ultimately not sufficient to establish their overall trustworthiness. This is because transparency procedures currently employed, such as the use of an interpretable predictor (IP),2 cannot fully overcome the opacity of such models. Computational reliabilism (CR), an alternate approach to adjudicating trustworthiness that goes beyond transparency solutions, is argued to be a more promising approach. CR can bring the benefits of traditional process reliabilism in epistemology to bear on this problem of model trustworthiness. Durán et al ’s explicitly reliabilist epistemology to assess the trustworthiness of black box models is a timely addition to current transparency-focused approaches in the literature. Their delineation of the epistemic from the ethical also serves the debate by clarifying the nature of the different problems. However, their overall account underestimates the epistemic value of certain transparency-enabling approaches by conflating different types of opacity and also oversimplifies transparency-advocating arguments in the literature. First, it is unclear why Durán et al consider transparency approaches as insufficient to overcome epistemic opacity, if heiraccount of opacity is the traditional one from the machine learning literature: opacity stemming from the mismatch between (1) mathematical optimisation in high dimensionality that is characteristic of machine learning and (2) the demands of human-scale reasoning and styles of semantic interpretation.3 …