Lena Keller
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lenakeller.bsky.social
Lena Keller
@lenakeller.bsky.social
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PostDoc @ Uni Potsdam, Germany | Research on student achievement & motivation, gender differences & intersectionality, research syntheses with educational large-scale assessments | she/her | https://www.lenakristinakeller.com
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Reposted by Lena Keller
imprs-life.bsky.social
🥂https://www.mpib-berlin.mpg.de/awards/graduate-program-with-a-future-imprs-life-to-be-established-permanently
Graduate program with a future: IMPRS LIFE to be established permanently
www.mpib-berlin.mpg.de
February 27, 2025 at 10:44 AM
Reposted by Lena Keller
stefanherzog.bsky.social
🌟🧠💪📝
#BOOSTING: Empowering citizens with behavioral science

New, freely available paper in Annual Review of Psychology.
PDF: tinyurl.com/boosting2025

For more: scienceofboosting.org

@arc-mpib.bsky.social @mpib-berlin.bsky.social

@annualreviews.bsky.social
#policy #behavioralscience

1/ 🧵👇
The image is the cover page of an article from the "Annual Review of Psychology" titled "Boosting: Empowering Citizens with Behavioral Science" by Stefan M. Herzog and Ralph Hertwig. It features a brief abstract, keywords, and publication details. The abstract outlines the concept of "boosting" as a behavioral public policy that emphasizes empowering individuals to make informed decisions, in contrast to "nudging," which subtly steers behavior. The abstract reads: Behavioral public policy came to the fore with the introduction of nudging, which aims to steer behavior while maintaining freedom of choice. Responding to critiques of nudging (e.g., that it does not promote agency and relies on benevolent choice architects), other behavioral policy approaches focus on empowering citizens. Here we review boosting, a behavioral policy approach that aims to foster people's agency, self-control, and ability to make informed decisions. It is grounded in evidence from behavioral science showing that human decision making is not as notoriously flawed as the nudging approach assumes. We argue that addressing the challenges of our time—such as climate change, pandemics, and the threats to liberal democracies and human autonomy posed by digital technologies and choice architectures—calls for fostering capable and engaged citizens as a first line of response to complement slower, systemic approaches. List with summary points: 1. Behavioral public policy garnered widespread attention with the introduction of nudging, which aims to steer behavior while maintaining freedom of choice. 2. Criticisms of nudging include that it does not promote agency and competences and that it relies—overly optimistically—on the presence of benevolent choice architects. 3. The proliferation of environments threatening people's autonomy, the slow pace of systemic approaches to tackling societal issues, and the intrinsic benefits of empowerment make empowering citizens an indispensable objective of behavioral public policy. 4. Boosting is a behavioral public policy approach to empowerment grounded in evidence from behavioral science that shows that humans’ boundedly rational decision making is not as flawed as the nudging approach assumes. 5. Boosts are interventions that improve people's competencies to make informed choices that conform to their goals, preferences, and desires. 6. In self-nudging boosts, people learn to use architectural changes in their proximate choice environment to regulate their own behavior—that is, they are empowered to adapt their own choice environments. 7. There are boosts to foster core competences in many domains, including finance, online environments, and health, as well as broader, overarching areas, such as motivation, risk, and judgment and decision making. Boosts should be part of a policy mix that also includes system-level approaches. 8. When implementing boosts, policy makers need to avoid the trap of individualizing responsibility and to be mindful that, due to differences in cognition and motivation, inequalities in the desirable effects across boosted individuals may emerge.
January 23, 2025 at 10:54 AM
Reposted by Lena Keller
stefanherzog.bsky.social
🌟🤖📝 **Boosting human competences with interpretable and explainable artificial intelligence**

How can AI *boost* human decision-making instead of replacing it? We talk about this in our new paper.

doi.org/10.1037/dec0...

#AI #XAI #InterpretableAI #IAI #boosting #competences
🧵👇
Article information Title: Boosting human competences with interpretable and explainable artificial intelligence. Full citation: Herzog, S. M., & Franklin, M. (2024). Boosting human competences with interpretable and explainable artificial intelligence. Decision, 11(4), 493–510. https://doi.org/10.1037/dec0000250 Abstract: Artificial intelligence (AI) is becoming integral to many areas of life, yet many—if not most—AI systems are opaque black boxes. This lack of transparency is a major source of concern, especially in high-stakes settings (e.g., medicine or criminal justice). The field of explainable AI (XAI) addresses this issue by explaining the decisions of opaque AI systems. However, such post hoc explanations are troubling because they cannot be faithful to what the original model computes—otherwise, there would be no need to use that black box model. A promising alternative is simple, inherently interpretable models (e.g., simple decision trees), which can match the performance of opaque AI systems. Because interpretable models represent—by design—faithful explanations of themselves, they empower informed decisions about whether to trust them. We connect research on XAI and inherently interpretable AI with that on behavioral science and boosts for competences. This perspective suggests that both interpretable AI and XAI could boost people’s competences to critically evaluate AI systems and their ability to make accurate judgments (e.g., medical diagnoses) in the absence of any AI support. Furthermore, we propose how to empirically assess whether and how AI support fosters such competences. Our theoretical analysis suggests that interpretable AI models are particularly promising and—because of XAI’s drawbacks—preferable. Finally, we argue that explaining large language models (LLMs) faces similar challenges as XAI for supervised machine learning and that the gist of our conjectures also holds for LLMs.
November 20, 2024 at 12:25 PM
Reposted by Lena Keller
veitkubik.bsky.social
🎓Promotionsstelle (100%, 4 Jahre) im Bereich “Lehren & Lernen mit digitalen Medien” @FU_Berlin! Forschung zu #Lasting Learning, #Bildungstechnologien, #DigitalLiteracy & mehr. 👩‍🏫💻📱Jetzt bewerben bis 21.10.24: tinyurl.com/23cvevz7 ✨ #Job #Wissenschaft #EdTech
Wissenschaftliche*r Mitarbeiter*in (E 13 TV-L, 4 Jahre, 100%-Stelle) zur Promotion Pädagogische Psychologie
Der Bereich des Lehrens und Lernens mit digitalen Medien befasst sich mit Forschung und Lehre in folgenden zentralen Forschungsschwerpunkten:- Nutzung von Bildungstechnologien zur Förderung kognitiver...
tinyurl.com
October 12, 2024 at 6:36 PM