Ethical machines: The human-centric use of artificial intelligence

Lepri, Bruno; Oliver, Núria; Pentland, Alex

doi:10.1016/j.isci.2021.102249

Cited by 65 publications

(42 citation statements)

References 156 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Human Centered AI institute,(https://hai.stanford.edu/) or the UKRI Trustworthy Autonomous Systems Hub. (http://www.tas.ac.uk/) From a technical perspective, human-centered (Lepri et al, 2021;Shneiderman, 2020;Wilson and Daugherty, 2018) and machine-centered approaches (Rahwan et al, 2019;Awad et al, 2018;Kraus et al, 2020) to the development of artificial intelligence have emerged. Human-centered approaches propose to develop socially beneficial and ethical machine intelligence that also augments human capabilities and undertakes human tasks with high reliability.…”

mentioning

confidence: 99%

Trustworthy human-AI partnerships

2021

View full text Add to dashboard Cite

In this paper, we foreground some of the key research challenges that arise in the design of trustworthy human-AI partnerships. In particular, we focus on the challenges in designing human-AI partnerships that need to be addressed to help humans and organizations trust their machine counterparts individually or as a collective (e.g., as robot teams or groups of software agents). We also aim to identify the risks associated with human-AI partnerships and therefore determine the associated measures to mitigate these risks. By so doing, we will trigger new avenues of research that will address the key barriers to the adoption of AI-based systems more widely in our daily lives and in industry. INTRODUCTIONRecent advances in Artificial Intelligence (AI), Machine Learning (ML), and Robotics have significantly enhanced the capabilities of machines. Machine intelligence is now able to support human decision making, augment human capabilities, and, in some cases, take over control from humans and act fully autonomously. Real-world examples of autonomous systems including self-driving vehicles, recommender systems, facial recognition systems, and automated trading are only just beginning to demonstrate the value machine intelligence can deliver to society and the wider economy. However, unfortunately, there have also been failures in the deployment of such autonomous systems that have resulted in fatal car crashes, plane accidents, and stock market failures (Brynjolfsson and McAfee, 2014;Daugherty and Wilson, 2018). Such failures are often attributed to a poor understanding of how to weave AI systems into our societal and industrial fabric.Given this, we believe that the next big advance for AI and ML systems will involve them being significantly more tightly embedded into systems alongside humans, interacting and influencing each other in a number of ways. Such human-AI partnerships are a new form of socio-technical system in which the potential synergies between humans and machines are much more fully utilized. To achieve this, AI systems will need to leave their currently solipsistic nature behind and be able to cooperate, coordinate, and compete with one another and their human interlocutors. Such partnerships will combine their complementary skills and capabilities to make the best use of the distinctive strengths of humans and machines (Licklider, 1960), while also acknowledging their potentially diverging preferences, purpose, and objectives that may give rise to conflict or cause them to attempt to influence (intentionally or not) each other's decision-making. Likewise, humans will likely be challenged to work and live with AI systems as fully autonomous partners, rather than purely tools that they can manipulate or query. The modalities through which they engage with AI systems will also vary greatly, shifting away from typical screen-based or tactile interfaces to voice or brain-controlled, opening new opportunities and risks for interactions between humans and AI systems. These elements of the human-AI partnersh...

show abstract

mentioning

confidence: 99%

Trustworthy human-AI partnerships

2021

View full text Add to dashboard Cite

show abstract

“…Although the potential impact of AI in DM is proved to be significant ( Zheng et al, 2019 ; Lepri et al, 2021 ), it led many practitioners and researchers in the field to take divergent points of view ( Duan et al, 2019 ). The debate on human/technology relationships, even in AI, is not new: since the end of the last century, prominent scholars in the field have started positioning on contrasting perspectives, so that we can distinguish techno-enthusiasts , the true believers and supporters of technology and post-humanity, and techno-skeptics , who are more cautious and critical about future AI implementation in DM.…”

Section: Ai Role In Dm: State Of the Artmentioning

confidence: 99%

“…On one hand, techno-enthusiasts believe that the objectivity conferred by technology is an added value because it reduces the variability of human error. Specifically, they argue that algorithmic DM processes can lead to more objective decisions than those made by humans, which may be influenced by individual bias, conflicts of interest, or fatigue ( Lepri et al, 2021 ). On the other hand, techno-skeptics firmly state that machines can only partially simulate but never duplicate the unique mental life of humans; in fact, machines cannot feel or understand the complexity of real-life situations ( Postman, 1993 ).…”

Section: Ai Role In Dm: State Of the Artmentioning

confidence: 99%

“…One of the main concerns of techno-skeptics is the lack of transparency of AI. In this regard, most skeptics criticize algorithmic DM processes for the threat of privacy invasion, information asymmetry, and discrimination ( Lepri et al, 2021 ). Moreover, AI and algorithmic DM processes are increasingly challenged for their black-box nature: although AI systems enable robust predictions, most users have little awareness and knowledge of how these systems make decisions.…”

Section: Ai Role In Dm: State Of the Artmentioning

confidence: 99%

See 1 more Smart Citation

“The Flow in the Funnel”: Modeling Organizational and Individual Decision-Making for Designing Financial AI-Based Systems

2021

View full text Add to dashboard Cite

Nowadays, the current application of artificial intelligence (AI) to financial context is opening a new field of study, named financial intelligence, in which the implementation of AI-based solutions as “financial brain” aims at assisting in complex decision-making (DM) processes as wealth and risk management, financial security, financial consulting, and blockchain. For venture capitalist organizations (VCOs), this aspect becomes even more critical, since different actors (shareholders, bondholders, management, suppliers, customers) with different DM behaviors are involved. One last layer of complexity is the potential variation of behaviors performed by managers even in presence of fixed organizational goals. The aim of this study is twofold: a general analysis of the debate on implementing AI in DM processes is introduced, and a proposal for modeling financial AI-based services is presented. A set of qualitative methods based on the application of cultural psychology is presented for modeling financial DM processes of all actors involved in the process, machines as well as individuals and organizations. The integration of some design thinking techniques with strategic organizational counseling supports the modeling of a hierarchy of selective criteria of fund-seekers and the creation of an innovative value proposition accordingly with goals of VCOs to be represented and supported in AI-based systems. Implications suggest that human/AI integration in the field can be implemented by developing systems where AI can be conceived in two distinct functions: (a) automation: treating Big Data from the market defined by management of VCO; and (b) support: creating alert systems that are coherent with ordered weighted decisional criteria of VCO.

show abstract

“…Cai et al, 2019). However, this solution has strong technical limitations, as demographic characteristics can creep into the algorithm indirectly (Lepri et al, 2021, Tomasev et al, 2021. For example, people from a certain country might combine two specific nonverbal behaviors much more often than expected, allowing the algorithm to "indirectly" recognize ethnicity.…”

Section: Integrative Approach To Personality Detection 22mentioning

confidence: 99%

Toward an Integrative Approach to Nonverbal Personality Detection: Connecting Psychological and Artificial Intelligence Research.

Cannata¹,

Breil²,

Back³

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

Our first impressions of the people we meet are the subject of considerable interest, academic and non-academic. Such initial estimates of another’s personality (e.g., their sociality or agreeableness) are vital, since they enable us to predict the outcomes of interactions (e.g., can we trust them?). Nonverbal behaviors are a key medium through which personality is expressed and detected. The character and reliability of these expression and detection processes have been investigated within two major fields: Psychological research on personality judgments accuracy and Artificial Intelligence research on personality computing. Communication between these fields has, however, been infrequent. In the present perspective, we summarize the contributions and open questions of both fields and propose an integrative approach to combine their strengths and overcome their limitations. The integrated framework will enable novel research programs, such as (i), identifying which detection tasks better suit humans or computers, (ii), harmonizing the nonverbal features extracted by humans and computers, and (iii), integrating human and artificial agents in hybrid systems.

show abstract

Ethical machines: The human-centric use of artificial intelligence

Cited by 65 publications

References 156 publications

Trustworthy human-AI partnerships

Trustworthy human-AI partnerships

“The Flow in the Funnel”: Modeling Organizational and Individual Decision-Making for Designing Financial AI-Based Systems

Toward an Integrative Approach to Nonverbal Personality Detection: Connecting Psychological and Artificial Intelligence Research.

Contact Info

Product

Resources

About