Gabriel Lima

How to attribute responsibility for autonomous artificial intelligence (AI) systems’ actions has been widely debated across the humanities and social science disciplines. This work presents two experiments (N=200 each) that measure people’s perceptions of eight different notions of moral responsibility concerning AI and human agents in the context of bail decision-making. Using real-life adapted vignettes, our experiments show that AI agents are held causally responsible and blamed similarly to

Whether to give rights to artificial intelligence (AI) and robots has been a sensitive topic since the European Parliament proposed advanced robots could be granted "electronic personalities." Numerous scholars who favor or disfavor its feasibility have participated in the debate. This paper presents an experiment (N=1270) that 1) collects online users' first impressions of 11 possible rights that could be granted to autonomous electronic agents of the future and 2) examines whether debunking co

Decision-making algorithms are being used in important decisions, such as who should be enrolled in health care programs and be hired. Even though these systems are currently deployed in high-stakes scenarios, many of them cannot explain their decisions. This limitation has prompted the Explainable Artificial Intelligence (XAI) initiative, which aims to make algorithms explainable to comply with legal requirements, promote trust, and maintain accountability. This paper questions whether and to w

Whether to give rights to artificial intelligence (AI) and robots has been a sensitive topic since the European Parliament proposed advanced robots could be granted "electronic personalities." Numerous scholars who favor or disfavor its feasibility have participated in the debate. This paper presents an experiment (N=1270) that 1) collects online users' first impressions of 11 possible rights that could be granted to autonomous electronic agents of the future and 2) examines whether debunking co

Artificial intelligence (AI) systems can cause harm to people. This research examines how individuals react to such harm through the lens of blame. Building upon research suggesting that people blame AI systems, we investigated how several factors influence people’s reactive attitudes towards machines, designers, and users. The results of three studies (N = 1,153) indicate differences in how blame is attributed to these actors. Whether AI systems were explainable did not impact blame directed at

Regulating artificial intelligence (AI) has become necessary in light of its deployment in high-risk scenarios. This paper explores the proposal to extend legal personhood to AI and robots, which had not yet been examined through the lens of the general public. We present two studies (<i>N</i> = 3,559) to obtain people's views of electronic legal personhood vis-à-vis existing liability models. Our study reveals people's desire to punish automated agents even though these entities are not recogni

The moral standing of robots and artificial intelligence (AI) systems has become a widely debated topic by normative research. This discussion, however, has primarily focused on those systems developed for social functions, e.g., social robots. Given the increasing interdependence of society with nonsocial machines, examining how existing normative claims could be extended to specific disrupted sectors, such as the art industry, has become imperative. Inspired by the proposals to ground machines

Determining who is responsible for online misinformation is an important problem. This research offers a multifaceted view of the public's perception of who is responsible for online misinformation. Via two studies, we surveyed how people attribute responsibility separately for creating, disseminating, and failing to prevent the dissemination of false information online. Study 1 (N=99) employed a mixed-methods approach to identify a series of actors deemed responsible for each aspect of misinfor

The possibility of extending legal personhood to artificial intelligence (AI) and robots has raised many questions on how these agents could be held liable given existing legal doctrines. Intending to promote a broader discussion, we conducted a survey (N=3315) asking online users' impressions of electronic agents' liability. Results suggest the existence of what we call the punishment gap that refers to the public's demand to punish automated agents upon a legal offense, even though their punis

Responsible Artificial Intelligence (AI) proposes a framework that holds all stakeholders involved in the development of AI to be responsible for their systems. It, however, fails to accommodate the possibility of holding AI responsible per se, which could close some legal and moral gaps concerning the deployment of autonomous and self-learning systems. We discuss three notions of responsibility (i.e., blameworthiness, accountability, and liability) for all stakeholders, including AI, and sugges

Vaccines for COVID-19 are currently under clinical trials. These vaccines are crucial for eradicating the novel coronavirus. Despite the potential, there exist conspiracies related to vaccines online, which can lead to vaccination hesitancy and, thus, a longer-standing pandemic. We used a between-subjects study design (N=572 adults in the US and UK) to understand the public willingness towards vaccination against the novel coronavirus under various circumstances. Our survey findings suggest that

The question of who should be held responsible when machines cause harm in high-risk environments is open to debate. Empirical research examining laypeople’s opinions has been largely restricted to the moral domain and has only inspected a limited set of negative outcomes. This study collects lay perceptions of legal responsibility for a wide range of machine-caused harms. We investigated how much people (N = 572) expect users and developers of machines to pay as legal damages in 37 diverse scen

This paper revisits the debate around the legal personhood of AI and robots, which has been highly sensitive yet important in the face of broad adoption of autonomous and self-learning systems. We conducted a survey ($N$=3,315) to understand lay people's perceptions of this topic and analyzed how they would assign responsibility, awareness, and punishment to AI, robots, humans, and various entities that could be held liable under existing doctrines. Even though people did not recognize any menta

The European Parliament's proposal to create a new legal status for artificial intelligence (AI) and robots brought into focus the idea of electronic legal personhood. This discussion, however, is hugely controversial. While some scholars argue that the proposed status could contribute to the coherence of the legal system, others say that it is neither beneficial nor desirable. Notwithstanding this prospect, we conducted a survey (N=3315) to understand online users' perceptions of the legal pers

As new gadgets that interact with the user through voice become accessible, the importance of not only the content of the speech increases, but also the significance of the way the user has spoken. Even though many techniques have been developed to indicate emotion on speech, none of them can fully grasp the real emotion of the speaker. This paper presents a neural network model capable of predicting emotions in conversations by analyzing transcriptions and raw audio waveforms, focusing on featu