The article studies how public policies of migrant ‘integration’ are enacted and made sense of in the street-level welfare state from the perspective of social work practitioners performing integration work and with a focus on language and language skills acquisition. It draws from twenty-seven semi-structured individual interviews and reflective discussions after service user meetings with eleven social workers and social advisors in migrant integration services conducted between 2018 and 2019 in the Helsinki capital region of Finland. Results infer ambiguous, yet persistent, ideals of monolingualism in which Finnish language skills acquisition plays a central role and linguistic diversity turns into individual lack of skills and capacities through service user responsibilisation. Yet practitioners refer to the unfeasible situations this creates for service users as well as to their own struggles as practitioners within the monolingual service system.

Murayama and Jach offer valuable suggestions for how to integrate computational processes into motivation theory, but these processes cannot do away with motivation altogether. Rewards are only rewarding because people want and like them – that is, because of motivation. Sexual desire is not primarily a quest for rewarding information. Elucidating the interface between motivation and cognition seems a promising way forward.

Although higher-level constructs often fail to explain the mechanisms underlying motivation, we argue that purely mechanistic approaches have limitations. Lower-level neural data help us identify “biologically plausible” mechanisms, while higher-level constructs are critical to formulate measurable behavioral outcomes when constructing computational models. Therefore, we propose that a multi-level, multi-measure approach is required to fully unpack the black box of motivated behavior.

Melina Constantine Bell (2021) argues that J. S. Mill's harm principle permits society to coercively interfere with the use of bigoted insults, since these insults are harmful on “a more expansive, modern, conception of harm.” According to Bell, these insults are harmful in virtue of their contributing to detrimental objective states like health problems. I argue that people with illiberal dispositions might have intense and sustained negative subjective reactions to behavior that the harm principle ought to protect, reactions intense enough to affect their health or other objective interests. Bell's way of thinking about harm therefore has illiberal implications. Yet I agree with her that bigoted insults should be regarded as harmful. I therefore propose an alternative way of understanding harm according to which subjective pain is a harm when it is intentionally caused.

Artificial intelligence (AI) is increasingly being integrated into sentencing within the criminal justice system. This research examines the impact of AI on sentencing, addressing the challenges and opportunities for fairness and justice. The main problem explored is AI’s potential to perpetuate biases, undermining fair-trial principles. This study intends to assess AI’s influence on sentencing, identify legal and ethical challenges, and propose a framework for equitable AI use in judicial decisions. Key research questions include: (1) How does AI influence sentencing decisions? (2) What concerns arise from AI in sentencing? (3) What safeguards can mitigate those concerns and prejudices? Utilizing qualitative methodology, including doctrinal analysis and comparative studies, the research reveals AI’s potential to enhance sentencing efficiency but also to risk reinforcing biases. The study recommends robust regulatory frameworks, transparency in AI algorithms, and judicial oversight to ensure AI supports justice rather than impedes it, advocating for a balanced integration that prioritizes human rights and fairness.