Sharpening the Scythe of Technological Change: Socio-Technical Challenges of Autonomous and Adaptive Cyber-Physical Systems

Cancila, Daniela; Gerstenmayer, Jean-Louis; Espinoza, Huáscar

doi:10.3390/designs2040052

Cited by 5 publications

(4 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To allow for a human-centered AI governance, one requires a dynamic responsive framework that is updatable by design [19] in the light of novel emerging sociotechnological [20][21][22] AI impacts. For this purpose, it has been postulated to combine proactive and reactive mechanisms in AI governance frameworks in order to achieve an effective socio-technological feedback-loop [19].…”

Section: Aims and Limitationsmentioning

confidence: 99%

Transdisciplinary AI Observatory—Retrospective Analyses and Future-Oriented Contradistinctions

2021

View full text Add to dashboard Cite

In the last years, artificial intelligence (AI) safety gained international recognition in the light of heterogeneous safety-critical and ethical issues that risk overshadowing the broad beneficial impacts of AI. In this context, the implementation of AI observatory endeavors represents one key research direction. This paper motivates the need for an inherently transdisciplinary AI observatory approach integrating diverse retrospective and counterfactual views. We delineate aims and limitations while providing hands-on-advice utilizing concrete practical examples. Distinguishing between unintentionally and intentionally triggered AI risks with diverse socio-psycho-technological impacts, we exemplify a retrospective descriptive analysis followed by a retrospective counterfactual risk analysis. Building on these AI observatory tools, we present near-term transdisciplinary guidelines for AI safety. As further contribution, we discuss differentiated and tailored long-term directions through the lens of two disparate modern AI safety paradigms. For simplicity, we refer to these two different paradigms with the terms artificial stupidity (AS) and eternal creativity (EC) respectively. While both AS and EC acknowledge the need for a hybrid cognitive-affective approach to AI safety and overlap with regard to many short-term considerations, they differ fundamentally in the nature of multiple envisaged long-term solution patterns. By compiling relevant underlying contradistinctions, we aim to provide future-oriented incentives for constructive dialectics in practical and theoretical AI safety research.

show abstract

Section: Aims and Limitationsmentioning

confidence: 99%

Transdisciplinary AI Observatory—Retrospective Analyses and Future-Oriented Contradistinctions

2021

View full text Add to dashboard Cite

show abstract

“…To allow for a human-centered AI governance, one requires a dynamic responsive framework that is updatable by design [19] in the light of novel emerging socio-technological [20][21][22] AI impacts. For this purpose, it has been postulated to combine proactive and reactive mechanisms in AI governance frameworks in order to achieve an effective socio-technological feedback-loop [19].…”

Section: Aims and Limitationsmentioning

confidence: 99%

Transdisciplinary AI Observatory -- Retrospective Analyses and Future-Oriented Contradistinctions

Aliman¹,

Kester²,

Yampolskiy³

2020

Preprint

View full text Add to dashboard Cite

In the last years, AI safety gained international recognition in the light of heterogeneous safety-critical and ethical issues that risk overshadowing the broad beneficial impacts of AI. In this context, the implementation of AI observatory endeavors represents one key research direction. This paper motivates the need for an inherently transdisciplinary AI observatory approach integrating diverse retrospective and counterfactual views. We delineate aims and limitations while providing hands-on-advice utilizing concrete practical examples. Distinguishing between unintentionally and intentionally triggered AI risks with diverse socio-psycho-technological impacts, we exemplify a retrospective descriptive analysis followed by a retrospective counterfactual risk analysis. Building on these AI observatory tools, we present near-term transdisciplinary guidelines for AI safety. As further contribution, we discuss differentiated and tailored long-term directions through the lens of two disparate modern AI safety paradigms. For simplicity, we refer to these two different paradigms with the terms artificial stupidity (AS) and eternal creativity (EC) respectively. While both AS and EC acknowledge the need for a hybrid cognitive-affective approach to AI safety and overlap with regard to many short-term considerations, they differ fundamentally in the nature of multiple envisaged long-term solution patterns. By compiling relevant underlying contradistinctions, we aim to provide future-oriented incentives for constructive dialectics in practical and theoretical AI safety research.

show abstract

“…The limitations of existing testing and safety verification methods for AVs and ML can be improved in multiple ways, such as through fault injection, which is a widely recognised tool used for assessing safety and validating corner-cases in fault-tolerant mechanisms in autonomous systems [151], such as by randomly modifying the weights of neural networks and simulating erroneous inputs for sensors and maps to find defects in AV software that might be activated in unexpected scenarios [147,151,152]. Synthesis approaches and formal verification tools are popular means to verify AV control systems but are limited in deployment due to their high computational costs.…”

Section: Safety Verification and Testingmentioning

confidence: 99%

“…• Improve testing and safety verification methods for AVs and ML algorithms: (1) Fault injection to identify unexpected defects, e.g., modifying neural networks, feeding erroneous inputs to sensors [147,151,152]. (2) Formal verification tools to verify controllers and networks [22,153].…”

mentioning

confidence: 99%

Algorithmic Decision-Making in AVs: Understanding Ethical and Technical Concerns for Smart Cities

Lim

Taeihagh

2019

Sustainability

View full text Add to dashboard Cite

Autonomous Vehicles (AVs) are increasingly embraced around the world to advance smart mobility and more broadly, smart, and sustainable cities. Algorithms form the basis of decision-making in AVs, allowing them to perform driving tasks autonomously, efficiently, and more safely than human drivers and offering various economic, social, and environmental benefits. However, algorithmic decision-making in AVs can also introduce new issues that create new safety risks and perpetuate discrimination. We identify bias, ethics, and perverse incentives as key ethical issues in the AV algorithms' decision-making that can create new safety risks and discriminatory outcomes. Technical issues in the AVs' perception, decision-making and control algorithms, limitations of existing AV testing and verification methods, and cybersecurity vulnerabilities can also undermine the performance of the AV system. This article investigates the ethical and technical concerns surrounding algorithmic decision-making in AVs by exploring how driving decisions can perpetuate discrimination and create new safety risks for the public. We discuss steps taken to address these issues, highlight the existing research gaps and the need to mitigate these issues through the design of AV's algorithms and of policies and regulations to fully realise AVs' benefits for smart and sustainable cities.Sustainability 2019, 11, 5791 2 of 28 surroundings and algorithms that process the data, make, and execute driving decisions through the vehicle's actuators. The quality of data retrieved by the AV's sensors is critical for decision-making [21], and the efficiency, precision and reliability of decision-making algorithms allows AVs to surpass the typical human driver in performing driving tasks [22]. A major component in AVs is machine-learning (ML) [23] algorithms that continuously learn and adapt to new information, which is essential for responding to unexpected situations and providing on-demand transportation services [24]. Algorithms' independence from human input and ML's data-driven nature allows AVs to significantly reduce or eliminate human errors that have been responsible for 90% of road fatalities, such as speeding, alcohol impairment, distractions and induced fear [25,26].However, an overemphasis on technological solutions alone for economic development could risk neglecting social and environmental considerations and thus hinder true "smartness" [27]. Scholars have cautioned against rushing to develop smart mobility solutions such as AVs without being prepared to manage their potential "negative externalities" [1,28]. In particular, issues in algorithmic decision-making in AVs can have undesirable effects on safety and equity. Firstly, data mining processes in AVs are susceptible to biases that lead algorithms to prioritise the safety of certain groups of road users over others and thus, perpetuate discrimination [29,30]. Secondly, many scholars stress the need to design algorithms with ethical considerations to ensure that AVs make ethical driving dec...

show abstract

Sharpening the Scythe of Technological Change: Socio-Technical Challenges of Autonomous and Adaptive Cyber-Physical Systems

Cited by 5 publications

References 35 publications

Transdisciplinary AI Observatory—Retrospective Analyses and Future-Oriented Contradistinctions

Transdisciplinary AI Observatory—Retrospective Analyses and Future-Oriented Contradistinctions

Transdisciplinary AI Observatory -- Retrospective Analyses and Future-Oriented Contradistinctions

Algorithmic Decision-Making in AVs: Understanding Ethical and Technical Concerns for Smart Cities

Contact Info

Product

Resources

About