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[This corrects the article DOI: 10.1371/journal.pone.0185651.].

In the current era, people and society have grown increasingly reliant on artificial intelligence (AI) technologies. AI has the potential to drive us towards a future in which all of humanity flourishes. It also comes with substantial risks for oppression and calamity. Discussions about whether we should (re)trust AI have repeatedly emerged in recent years and in many quarters, including industry, academia, healthcare, services, and so on. Technologists and AI researchers have a responsibility to develop trustworthy AI systems. They have responded with great effort to design more responsible AI algorithms. However, existing technical solutions are narrow in scope and have been primarily directed towards algorithms for scoring or classification tasks, with an emphasis on fairness and unwanted bias. To build long-lasting trust between AI and human beings, we argue that the key is to think beyond algorithmic fairness and connect major aspects of AI that potentially cause AI’s indifferent behavior. In this survey, we provide a systematic framework of Socially Responsible AI Algorithms that aims to examine the subjects of AI indifference and the need for socially responsible AI algorithms, define the objectives, and introduce the means by which we may achieve these objectives. We further discuss how to leverage this framework to improve societal well-being through protection, information, and prevention/mitigation. This article appears in the special track on AI & Society.

Trust in science increases when scientists and the outlets certifying their work honor science’s norms. Scientists often fail to signal to other scientists and, perhaps more importantly, the public that these norms are being upheld. They could do so as they generate, certify, and react to each other’s findings: for example, by promoting the use and value of evidence, transparent reporting, self-correction, replication, a culture of critique, and controls for bias. A number of approaches for authors and journals would lead to more effective signals of trustworthiness at the article level. These include article badging, checklists, a more extensive withdrawal ontology, identity verification, better forward linking, and greater transparency.

Behavioral experiments on the trust game have shown that trust and trustworthiness are commonly seen among human beings, contradicting the prediction by assuming Homo economicus in orthodox Economics. This means some mechanism must be at work that favors their emergence. Most previous explanations, however, need to resort to some exogenous factors based upon imitative learning, a simple version of social learning. Here, we turn to the paradigm of reinforcement learning, where individuals revise their strategies by evaluating the long-term return through accumulated experience. Specifically, we investigate the trust game with the Q -learning algorithm, where each participant is associated with two evolving Q -tables that guide one’s decision-making as trustor and trustee, respectively. In the pairwise scenario, we reveal that high levels of trust and trustworthiness emerge when individuals appreciate both their historical experience and returns in the future. Mechanistically, the evolution of the Q -tables shows a crossover that resembles human psychological changes. We also provide the phase diagram for the game parameters, where the boundary analysis is conducted. These findings are robust when the scenario is extended to a latticed population. Our results thus provide a natural explanation for the emergence of trust and trustworthiness, and indicate that the long-ignored endogenous factors alone are sufficient to drive. More importantly, the proposed paradigm shows the potential to decipher many puzzles in human behaviors.

Artificial intelligence (AI)–synthesized text, audio, image, and video are being weaponized for the purposes of nonconsensual intimate imagery, financial fraud, and disinformation campaigns. Our evaluation of the photorealism of AI-synthesized faces indicates that synthesis engines have passed through the uncanny valley and are capable of creating faces that are indistinguishable— and more trustworthy—than real faces.

Software trustworthiness allocation and reallocation are the symmetry of software trustworthiness measure. They can provide the optimization scheme for trustworthiness development and improvement, according to the requirements. The existing allocation and reallocation models do not consider the absolute majority of software trustworthiness classification; therefore, they cannot be very accurate in the allocation and reallocation of software trustworthiness. In this paper, improved allocation and reallocation models are constructed, which can resolve the above problem, and their polynomial solving algorithms are designed. At the same time, a demonstration application of the improved models and algorithms is given, and the trustworthiness enhancement specification of spacecraft software, based on factory reports, is established, including trustworthiness development specification and trustworthiness improvement specification. This enhancement specification provides a scientific and reasonable theory and method for the delivery acceptance of spacecraft software, from qualitative to quantitative grading acceptance, and furnishes a standard guarantee for the trustworthy development and improvement of such software.

The global concept of One Health has gained popularity, with countries like Indonesia implementing policies to combat the pandemic. Despite this, many Indonesians have been slow to follow pandemic-related measures, such as getting vaccinated and social distancing. This study aims to investigate the connection between people’s experiences, their understanding of One Health, and their willingness to follow government policies to prevent and control pandemics. Using numerically scaled surveys, we analyzed the relationships between these variables. We recruited 224 participants from 19 provinces in Indonesia. Our results show a strong link between trust built through experience and understanding of One Health, and people’s compliance with government policies. This study emphasizes the importance of educating the public about One Health and building trust to improve adherence to future pandemic prevention measures.

The access of new power service terminals brings about a new demand for cross-domain and cross-system security access. Therefore, this paper proposes a cross-domain authentication and authorization model for a new power system based on trust management, which is realized through the cooperation of certificate authority, authentication module, authorization module, and trust management module. Firstly, the model proposed in this paper includes a cross-domain authentication method, which realizes authentication between domains by issuing and verifying tokens for terminals with sufficient trust. Secondly, the model includes an authorization method based on attribute and trust value, which realizes the authorization of the terminal through attribute policy and trust evaluation. Finally, a trust value calculation method for terminal cross-domain access is included in the model. In this paper, the proposed model and method are simulated to demonstrate the correctness and effectiveness of the model. The experimental results show that the model has the advantages of universality, efficiency, and ease of management, and the terminal can access resources safely and efficiently across domains.

Out-of-distribution (OOD) detection is critical to ensuring the reliability and safety of machine learning systems. For instance, in autonomous driving, we would like the driving system to issue an alert and hand over the control to humans when it detects unusual scenes or objects that it has never seen during training time and cannot make a safe decision. The term, OOD detection, first emerged in 2017 and since then has received increasing attention from the research community, leading to a plethora of methods developed, ranging from classification-based to density-based to distance-based ones. Meanwhile, several other problems, including anomaly detection (AD), novelty detection (ND), open set recognition (OSR), and outlier detection (OD), are closely related to OOD detection in terms of motivation and methodology. Despite common goals, these topics develop in isolation, and their subtle differences in definition and problem setting often confuse readers and practitioners. In this survey, we first present a unified framework called generalized OOD detection, which encompasses the five aforementioned problems, i.e.,AD, ND, OSR, OOD detection, and OD. Under our framework, these five problems can be seen as special cases or sub-tasks, and are easier to distinguish. Despite comprehensive surveys of related fields, the summarization of OOD detection methods remains incomplete and requires further advancement. This paper specifically addresses the gap in recent technical developments in the field of OOD detection. It also provides a comprehensive discussion of representative methods from other sub-tasks and how they relate to and inspire the development of OOD detection methods. The survey concludes by identifying open challenges and potential research directions.