Perspectives on Psychological Science最新文献_第2页

Why DON'T We "Say Her Name"? An Intersectional Model of the Invisibility of Police Violence Against Black Women and Girls. 为什么我们不 "说出她的名字"？警察暴力侵害黑人妇女和女孩行为隐匿性的交叉模型》。

IF 10.5 1区心理学

Perspectives on Psychological Science Pub Date : 2024-10-09 DOI: 10.1177/17456916241277554

Aerielle M Allen, Alexis Drain, Chardée A Galán, Azaadeh Goharzad, Irene Tung, Beza M Bekele

{"title":"Why DON'T We \"Say Her Name\"? An Intersectional Model of the Invisibility of Police Violence Against Black Women and Girls.","authors":"Aerielle M Allen, Alexis Drain, Chardée A Galán, Azaadeh Goharzad, Irene Tung, Beza M Bekele","doi":"10.1177/17456916241277554","DOIUrl":"https://doi.org/10.1177/17456916241277554","url":null,"abstract":"Racialized police violence is a profound form of systemic oppression affecting Black Americans, yet the narratives surrounding police brutality have disproportionately centered on Black men and boys, overshadowing the victimization of Black women and girls. In 2014, the #SayHerName campaign emerged to bring attention to the often-overlooked instances of police brutality against Black women and girls, including incidents of both nonsexual and sexual violence. Despite these efforts, mainstream discourse and psychological scholarship on police violence continue to marginalize the experiences of Black women and girls. This raises a critical question: Why DON'T we \"Say Her Name\"? This article employs intersectional frameworks to demonstrate how the historic and systemic factors that render Black women and girls particularly vulnerable to police violence also deny their legitimacy as victims, perpetuate their invisibility, and increase their susceptibility to state-sanctioned violence. We extend models of intersectional invisibility by arguing that ideologies related to age, in addition to racial and gender identities, contribute to their marginalization. Finally, we reflect on how psychological researchers can play a pivotal role in dismantling the invisibility of Black women and girls through scientific efforts and advocacy.","PeriodicalId":19757,"journal":{"name":"Perspectives on Psychological Science","volume":" ","pages":"17456916241277554"},"PeriodicalIF":10.5,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142392320","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Challenges in Understanding Human-Algorithm Entanglement During Online Information Consumption. 理解在线信息消费过程中人与算法纠缠的挑战。

IF 10.5 1区心理学

Perspectives on Psychological Science Pub Date : 2024-09-01 Epub Date: 2023-07-10 DOI: 10.1177/17456916231180809

Stephan Lewandowsky, Ronald E Robertson, Renee DiResta

{"title":"Challenges in Understanding Human-Algorithm Entanglement During Online Information Consumption.","authors":"Stephan Lewandowsky, Ronald E Robertson, Renee DiResta","doi":"10.1177/17456916231180809","DOIUrl":"10.1177/17456916231180809","url":null,"abstract":"Most content consumed online is curated by proprietary algorithms deployed by social media platforms and search engines. In this article, we explore the interplay between these algorithms and human agency. Specifically, we consider the extent of entanglement or coupling between humans and algorithms along a continuum from implicit to explicit demand. We emphasize that the interactions people have with algorithms not only shape users' experiences in that moment but because of the mutually shaping nature of such systems can also have longer-term effects through modifications of the underlying social-network structure. Understanding these mutually shaping systems is challenging given that researchers presently lack access to relevant platform data. We argue that increased transparency, more data sharing, and greater protections for external researchers examining the algorithms are required to help researchers better understand the entanglement between humans and algorithms. This better understanding is essential to support the development of algorithms with greater benefits and fewer risks to the public.","PeriodicalId":19757,"journal":{"name":"Perspectives on Psychological Science","volume":" ","pages":"758-766"},"PeriodicalIF":10.5,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11373152/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9765071","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Psychological AI: Designing Algorithms Informed by Human Psychology. 心理人工智能：根据人类心理学设计算法》（Psychological AI: Designing Algorithms Informed by Human Psychology）。

IF 10.5 1区心理学

Perspectives on Psychological Science Pub Date : 2024-09-01 Epub Date: 2023-07-31 DOI: 10.1177/17456916231180597

Gerd Gigerenzer

引用次数: 0

The Sins of the Parents Are to Be Laid Upon the Children: Biased Humans, Biased Data, Biased Models. 父母的罪过要加诸于子女：有偏见的人类、有偏见的数据、有偏见的模型。

IF 10.5 1区心理学

Perspectives on Psychological Science Pub Date : 2024-09-01 Epub Date: 2023-07-18 DOI: 10.1177/17456916231180099

Merrick R Osborne, Ali Omrani, Morteza Dehghani

{"title":"The Sins of the Parents Are to Be Laid Upon the Children: Biased Humans, Biased Data, Biased Models.","authors":"Merrick R Osborne, Ali Omrani, Morteza Dehghani","doi":"10.1177/17456916231180099","DOIUrl":"10.1177/17456916231180099","url":null,"abstract":"Technological innovations have become a key driver of societal advancements. Nowhere is this more evident than in the field of machine learning (ML), which has developed algorithmic models that shape our decisions, behaviors, and outcomes. These tools have widespread use, in part, because they can synthesize massive amounts of data to make seemingly objective recommendations. Yet, in the past few years, the ML community has been drawing attention to the need for caution when interpreting and using these models. This is because these models are created by humans, from data generated by humans, whose psychology allows for various biases that impact how the models are developed, trained, tested, and interpreted. As psychologists, we thus face a fork in the road: Down the first path, we can continue to use these models without examining and addressing these critical flaws and rely on computer scientists to try to mitigate them. Down the second path, we can turn our expertise in bias toward this growing field, collaborating with computer scientists to reduce the models' deleterious outcomes. This article serves to light the way down the second path by identifying how extant psychological research can help examine and curtail bias in ML models.","PeriodicalId":19757,"journal":{"name":"Perspectives on Psychological Science","volume":" ","pages":"796-807"},"PeriodicalIF":10.5,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10185871","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Transmission Versus Truth, Imitation Versus Innovation: What Children Can Do That Large Language and Language-and-Vision Models Cannot (Yet). 传播与真理，模仿与创新：孩子能做什么，大型语言和语言视觉模型（目前）做不到。

IF 10.5 1区心理学

Perspectives on Psychological Science Pub Date : 2024-09-01 Epub Date: 2023-10-26 DOI: 10.1177/17456916231201401

Eunice Yiu, Eliza Kosoy, Alison Gopnik

{"title":"Transmission Versus Truth, Imitation Versus Innovation: What Children Can Do That Large Language and Language-and-Vision Models Cannot (Yet).","authors":"Eunice Yiu, Eliza Kosoy, Alison Gopnik","doi":"10.1177/17456916231201401","DOIUrl":"10.1177/17456916231201401","url":null,"abstract":"Much discussion about large language models and language-and-vision models has focused on whether these models are intelligent agents. We present an alternative perspective. First, we argue that these artificial intelligence (AI) models are cultural technologies that enhance cultural transmission and are efficient and powerful imitation engines. Second, we explore what AI models can tell us about imitation and innovation by testing whether they can be used to discover new tools and novel causal structures and contrasting their responses with those of human children. Our work serves as a first step in determining which particular representations and competences, as well as which kinds of knowledge or skill, can be derived from particular learning techniques and data. In particular, we explore which kinds of cognitive capacities can be enabled by statistical analysis of large-scale linguistic data. Critically, our findings suggest that machines may need more than large-scale language and image data to allow the kinds of innovation that a small child can produce.","PeriodicalId":19757,"journal":{"name":"Perspectives on Psychological Science","volume":" ","pages":"874-883"},"PeriodicalIF":10.5,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11373165/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"54230419","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Three Challenges for AI-Assisted Decision-Making. 人工智能辅助决策面临的三大挑战。

IF 10.5 1区心理学

Perspectives on Psychological Science Pub Date : 2024-09-01 Epub Date: 2023-07-13 DOI: 10.1177/17456916231181102

Mark Steyvers, Aakriti Kumar

{"title":"Three Challenges for AI-Assisted Decision-Making.","authors":"Mark Steyvers, Aakriti Kumar","doi":"10.1177/17456916231181102","DOIUrl":"10.1177/17456916231181102","url":null,"abstract":"Artificial intelligence (AI) has the potential to improve human decision-making by providing decision recommendations and problem-relevant information to assist human decision-makers. However, the full realization of the potential of human-AI collaboration continues to face several challenges. First, the conditions that support complementarity (i.e., situations in which the performance of a human with AI assistance exceeds the performance of an unassisted human or the AI in isolation) must be understood. This task requires humans to be able to recognize situations in which the AI should be leveraged and to develop new AI systems that can learn to complement the human decision-maker. Second, human mental models of the AI, which contain both expectations of the AI and reliance strategies, must be accurately assessed. Third, the effects of different design choices for human-AI interaction must be understood, including both the timing of AI assistance and the amount of model information that should be presented to the human decision-maker to avoid cognitive overload and ineffective reliance strategies. In response to each of these three challenges, we present an interdisciplinary perspective based on recent empirical and theoretical findings and discuss new research directions.","PeriodicalId":19757,"journal":{"name":"Perspectives on Psychological Science","volume":" ","pages":"722-734"},"PeriodicalIF":10.5,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11373149/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9770751","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Social Drivers and Algorithmic Mechanisms on Digital Media. 数字媒体的社会驱动力和算法机制。

IF 10.5 1区心理学

Perspectives on Psychological Science Pub Date : 2024-09-01 Epub Date: 2023-07-19 DOI: 10.1177/17456916231185057

Hannah Metzler, David Garcia

{"title":"Social Drivers and Algorithmic Mechanisms on Digital Media.","authors":"Hannah Metzler, David Garcia","doi":"10.1177/17456916231185057","DOIUrl":"10.1177/17456916231185057","url":null,"abstract":"On digital media, algorithms that process data and recommend content have become ubiquitous. Their fast and barely regulated adoption has raised concerns about their role in well-being both at the individual and collective levels. Algorithmic mechanisms on digital media are powered by social drivers, creating a feedback loop that complicates research to disentangle the role of algorithms and already existing social phenomena. Our brief overview of the current evidence on how algorithms affect well-being, misinformation, and polarization suggests that the role of algorithms in these phenomena is far from straightforward and that substantial further empirical research is needed. Existing evidence suggests that algorithms mostly reinforce existing social drivers, a finding that stresses the importance of reflecting on algorithms in the larger societal context that encompasses individualism, populist politics, and climate change. We present concrete ideas and research questions to improve algorithms on digital platforms and to investigate their role in current problems and potential solutions. Finally, we discuss how the current shift from social media to more algorithmically curated media brings both risks and opportunities if algorithms are designed for individual and societal flourishing rather than short-term profit.","PeriodicalId":19757,"journal":{"name":"Perspectives on Psychological Science","volume":" ","pages":"735-748"},"PeriodicalIF":10.5,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11373151/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9822531","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

People Think That Social Media Platforms Do (but Should Not) Amplify Divisive Content. 人们认为社交媒体平台确实（但不应该）放大了分裂性的内容。

IF 10.5 1区心理学

Perspectives on Psychological Science Pub Date : 2024-09-01 Epub Date: 2023-09-26 DOI: 10.1177/17456916231190392

Steve Rathje, Claire Robertson, William J Brady, Jay J Van Bavel

{"title":"People Think That Social Media Platforms Do (but Should Not) Amplify Divisive Content.","authors":"Steve Rathje, Claire Robertson, William J Brady, Jay J Van Bavel","doi":"10.1177/17456916231190392","DOIUrl":"10.1177/17456916231190392","url":null,"abstract":"Recent studies have documented the type of content that is most likely to spread widely, or go \"viral,\" on social media, yet little is known about people's perceptions of what goes viral or what should go viral. This is critical to understand because there is widespread debate about how to improve or regulate social media algorithms. We recruited a sample of participants that is nationally representative of the U.S. population (according to age, gender, and race/ethnicity) and surveyed them about their perceptions of social media virality (n = 511). In line with prior research, people believe that divisive content, moral outrage, negative content, high-arousal content, and misinformation are all likely to go viral online. However, they reported that this type of content should not go viral on social media. Instead, people reported that many forms of positive content-such as accurate content, nuanced content, and educational content-are not likely to go viral even though they think this content should go viral. These perceptions were shared among most participants and were only weakly related to political orientation, social media usage, and demographic variables. In sum, there is broad consensus around the type of content people think social media platforms should and should not amplify, which can help inform solutions for improving social media.","PeriodicalId":19757,"journal":{"name":"Perspectives on Psychological Science","volume":" ","pages":"781-795"},"PeriodicalIF":10.5,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41109994","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The Inversion Problem: Why Algorithms Should Infer Mental State and Not Just Predict Behavior. 反转问题为什么算法应该推断心理状态，而不仅仅是预测行为？

IF 10.5 1区心理学

Perspectives on Psychological Science Pub Date : 2024-09-01 Epub Date: 2023-12-12 DOI: 10.1177/17456916231212138

Jon Kleinberg, Jens Ludwig, Sendhil Mullainathan, Manish Raghavan

引用次数: 0

Blinding to Circumvent Human Biases: Deliberate Ignorance in Humans, Institutions, and Machines. 蒙蔽以规避人类偏见：人类、机构和机器的故意无知。

IF 10.5 1区心理学

Perspectives on Psychological Science Pub Date : 2024-09-01 Epub Date: 2023-09-05 DOI: 10.1177/17456916231188052

Ralph Hertwig, Stefan M Herzog, Anastasia Kozyreva

{"title":"Blinding to Circumvent Human Biases: Deliberate Ignorance in Humans, Institutions, and Machines.","authors":"Ralph Hertwig, Stefan M Herzog, Anastasia Kozyreva","doi":"10.1177/17456916231188052","DOIUrl":"10.1177/17456916231188052","url":null,"abstract":"Inequalities and injustices are thorny issues in liberal societies, manifesting in forms such as the gender-pay gap; sentencing discrepancies among Black, Hispanic, and White defendants; and unequal medical-resource distribution across ethnicities. One cause of these inequalities is implicit social bias-unconsciously formed associations between social groups and attributions such as \"nurturing,\" \"lazy,\" or \"uneducated.\" One strategy to counteract implicit and explicit human biases is delegating crucial decisions, such as how to allocate benefits, resources, or opportunities, to algorithms. Algorithms, however, are not necessarily impartial and objective. Although they can detect and mitigate human biases, they can also perpetuate and even amplify existing inequalities and injustices. We explore how a philosophical thought experiment, Rawls's \"veil of ignorance,\" and a psychological phenomenon, deliberate ignorance, can help shield individuals, institutions, and algorithms from biases. We discuss the benefits and drawbacks of methods for shielding human and artificial decision makers from potentially biasing information. We then broaden our discussion beyond the issues of bias and fairness and turn to a research agenda aimed at improving human judgment accuracy with the assistance of algorithms that conceal information that has the potential to undermine performance. Finally, we propose interdisciplinary research questions.","PeriodicalId":19757,"journal":{"name":"Perspectives on Psychological Science","volume":" ","pages":"849-859"},"PeriodicalIF":10.5,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11373160/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10157746","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0