Caring in the in-between: a proposal to introduce responsible AI and robotics to healthcare

Abstract

In the scenario of growing polarization of promises and dangers that surround artificial intelligence (AI), how to introduce responsible AI and robotics in healthcare? In this paper, we develop an ethical–political approach to introduce democratic mechanisms to technological development, what we call “Caring in the In-Between”. Focusing on the multiple possibilities for action that emerge in the realm of uncertainty, we propose an ethical and responsible framework focused on care actions in between fears and hopes. Using the theoretical perspective of Science and Technology Studies and empirical research, “Caring in the In-Between” is based on three movements: the first is a change of focus from the world of promises and dangers to the world of uncertainties; the second is a conceptual shift from assuming a relationship with robotics based on a Human–Robot Interaction to another focused on the network in which the robot is embedded (the “Robot Embedded in a Network”); and the last is an ethical shift from a general normative framework to a discussion on the context of use. Based on these suggestions, “Caring in the In-Between” implies institutional challenges, as well as new practices in healthcare systems. It is articulated around three simultaneous processes, each of them related to practical actions in the “in-between” dimensions considered: monitoring relations and caring processes, through public engagement and institutional changes; including concerns and priorities of stakeholders, with the organization of participatory processes and alternative forms of representation; and making fears and hopes commensurable, through the choice of progressive and reversible actions.

Robotic Process Automation in Healthcare Sector

“…The purpose of such machines is to aid sufferers maintain their feelings of autonomy and worth. Robotics are additionally being created to aid people with private duties such as accessing the bathroom and grooming .The fear that robotics treatment could be innately inferior to carers since current robotics are unable to demonstrate true caring and empathy, either the machine performs physiologically helpful activities or familial/emotionally helpful activities for an individual [7].…”

Assessing Deep Learning: A Work Program for the Humanities in the Age of Artificial Intelligence

“…From a holistic view of the human person, the greater danger seems to be that human beings believe this and then treat each other as if they were reducible to such data and statistical analyses, profiles, and predictions drawn from them-this is bracketing out the fact that treating human beings in such a way can be both extremely effective and dehumanizing at the same time. Thus, an ethical assessment of the use of DL, for example, in profiling and predicting behavior-which already finds practical application, e.g., in law, insurance, loan giving, and health care (see, e.g., [182,331,[387][388][389][390][391][392][393])-would focus on the insight that such predictions and profiling can never do justice to human beings, their dignity, and freedom as persons and citizens of our societies. This would be an anthropological analysis, backing the ethical objection to the abusive instrumentalization of DL, rather than just an ethical objection that such abuse should not happen.…”

Are AI systems biased against the poor? A machine learning analysis using Word2Vec and GloVe embeddings

“…AI systems are not ethically neutral but, more and more, we are all dependent on AI for our decisions (Fry 2018 ). In the information society, AI is at the core of high risk services, such as healthcare (Watson et al 2019 ; Zetterholm et al 2021 ; Vallès-Peris and Domènech 2021 ), financial services (Kostka 2019 ; Townson 2020 ; Lee and Floridi 2020 ; Aggarwal 2020 ; Anshari et al 2021 ) justice and security (Poitras 2014 ; Hauge et al 2016 ; Merler et al 2019 ; Green et al 2019 ) and even the military (de Vynck 2021 ). AI is also an integral part of marketing, predicting users’ interests through big data that contain each person’s personal digital profile, in what has been called “surveillance capitalism” (Zuboff 2019 ).…”

“…It is widely documented that Artificial Intelligence (AI) reproduces and often amplifies biases against historically disempowered groups (Bolukbasi et al 2016;Garga et al 2018;Manzini et al 2019;Nadeem et al 2020). This constitutes a risk for the exacerbation of those biases offline and the eventual increase in discrimination (Vinuesa et al 2020). AI systems are not ethically neutral but, more and more, we are all dependent on AI for our decisions (Fry 2018).…”

“…However, to translate the principle of AI fairness (HLE-GAI 2019; European Commission 2021), into an operational reality, an in-depth analysis is required, far from the existing turmoil of quick-fix solutions. Bias within AI systems is only the tip of the iceberg, since AI reproduces the prejudices of the societies where they are trained (West et al 2019;Vinuesa et al 2020) in an unsupervised manner (Radford et al 2019;Talmor et al 2021), either within the data (Rudinger et al 2018;Chiappa et al 2020), the algorithms (Mittelstadt et al 2016;Tsamados et al 2021b) or even as a result of development procedures (Floridi 2019a;Vakkuri et al 2020). Therefore, trying to solve the AI ethical problems only through a technical approach is clearly insufficient, since it only has a superficial impact on fundamental inequalities (Zajko 2021).…”