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AbstractThroughout our history, we, Homo sapiens, have used technologies to better satisfy ourneeds. The relation betweenneedsandtechnologyis so fundamental that the US National Research Council defines the distinguishing characteristic of technology as its goal "to make modifications in the world [in order] to meet human needs" [1]. Artificial intelligence (AI) is one of the most promising emerging technologies of our time. Similar to other technologies, AI is expected by many "to meet [human] needs". In this article, we reflect on the relationship betweenneedsand AI, and call for the realization ofneeds-awareAI systems. We argue that re-thinkingneedsfor,through,by, andwithAI can be a very useful means towards the development of realistic approaches for sustainableHuman-aware,Accountable,Lawful, andEthical (HALE) AI systems. We discuss some of the most critical gaps, barriers, enablers, and drivers of co-creating future AI-based sociotechnical systems in which [human]needsare well considered and met. Finally, we provide an overview of potential challenges and considerations that should be carefully taken into account; and call for joint, immediate, and interdisciplinary efforts and collaborations to start on the path toneeds-awareAI.

Consent is meant to empower users by giving them a choice regarding the use of their personal data. Thus, organisations have the incentive to provide benefits, whether directly or indirectly, in return for consent. However, recent research has demonstrated the scale and scope of malpractices regarding consent on the web, where users are misled and coerced into giving away their personal data and privacy. In light of this, we call for investigating what benefits, if any, are specified in the context of consent; who benefits from it; and whether they can be observed in reality. We hope our work outlines the need to formally investigate the claims made when requesting consent and empowers users through greater transparency regarding benefits to make better-informed choices. ; This work is partially supported by the Internet Foundation Austria (IPA) within the NetIdee call (RESPECTeD Project; Grant#prj4625). Harshvardhan J. Pandit is funded by the Irish Research Council Government of Ireland Postdoctoral Fellowship under Grant#GOIPD/2020/790; European Union's Horizon 2020 research and innovation programme under NGI TRUST Grant#825618 for Project#3.40 Privacy-as-Expected: Consent Gateway; and as part of the ADAPT SFI Centre for Digital Media Technology which is funded by Science Foun- dation Ireland through the SFI Research Centres Programme and is co-funded under the European Regional Development Fund (ERDF) through Grant#13/RC/2106 P2.

In our increasingly digital societies, many companies have business models that perceive users' (or customers') personal data as a siloed resource, owned and controlled by the data controller rather than the data subjects. Collecting and processing such a massive amount of personal data could have many negative technical, social and economic consequences, including invading people's privacy and autonomy. As a result, regulations such as the European General Data Protection Regulation (GDPR) have tried to take steps towards a better implementation of the right to digital privacy. This paper proposes that such legal acts should be accompanied by the development of complementary technical solutions such as Cognitive Personal Assistant Systems to support people to effectively manage their personal data processing on the Internet. Considering the importance and sensitivity of personal data processing, such assistant systems should not only consider their owner's needs and values, but also be transparent, accountable and controllable. Pluralist approaches in computational cognitive modelling of human needs and values which are not bound to traditional paradigmatic borders such as cognitivism, connectionism, or enactivism, we argue, can create a balance between practicality and usefulness, on the one hand, and transparency, accountability, and controllability, on the other, while supporting and empowering humans in the digital world. Considering the threat to digital privacy as significant to contemporary democracies, the future implementation of such pluralist models could contribute to power-balance, fairness and inclusion in our societies.

This specification defines a mechanism for expressing user decisions about personal data processing under the European Union's data protection regulations, and similar regulations outside the EU. The mechanism functions through the exchange of HTTP headers between the user agent and the web server, or through an equivalent JavaScript interface. The mechanism serves as an automated means for users to give or refuse consent, to withdraw any consent already given, as well as to object to processing. The mechanism provides an alternative to existing nonautomated consent management approaches (e.g. 'cookie banners') and aims to reduce the efforts of the different parties involved regarding the protection of users' privacy. ; Series: Sustainable Computing Reports and Specifications