Critical Thinking Education and Debiasing (AILACT Essay Prize Winner 2013)

Kenyon, Tim; Beaulac, Guillaume

doi:10.22329/il.v34i4.4203

Cited by 51 publications

(17 citation statements)

References 34 publications

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“…It seems to conflict with the intuitive approach which says that critical thinking is closely related to debiasing. Kenyon and Beaulac (2014) said that critical thinking ability is inadequately reliable to ensure debiasing when an individual makes a decision or judgment. Further, according to the argumentative theory, reasoning ability often causes individuals to be more biased if they have already had a particular standpoint beforehand (Mercier & Sperber, 2011).…”

Section: Discussionmentioning

confidence: 99%

Partisan Selective Exposure to Fake News Content

Ardi¹

2019

Makara Hubs-Asia

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Selective exposure to political news in social media in Indonesia is escalating along with the increasing polarization of Indonesian people. This research aims to investigate: 1) differences in selective exposure to fake news content among incumbent and opposition supporters; 2) the association between critical thinking ability and partisans' selective exposure. Repeated measures design was employed as the experiment design. Respondents were student activists of extra-campus organizations with particular political ideologies, who have pro-incumbent or pro-opposition preference. Seventy-one respondents were recruited, consisting of 34 incumbent (Jokowi) supporters and 37 opposition (Prabowo) supporters. Data was analyzed using independent t-test, a paired sample t-test, and correlational analysis. Results show that the opposition side was more inclined to demonstrate selective exposure by believing in fake news about their political enemy, compared to the incumbent supporters. This is shown by their tendency to believe and spread discrediting news about their political opponents rather than doing so for news which discredits their side. No association between critical thinking and partisan selective exposure was found. The implication of these results is that the critical point in debiasing is not necessarily predicated merely on analytical thinking ability but might also rest on one's ability to think open-mindedly. Selective Exposure Partisan pada Konten-konten Berita Palsu Abstrak Fenomena terjadinya selective exposure atas berita-berita politik di media sosial di Indonesia semakin menguat seiring tajamnya polarisasi di antara masyarakat di Indonesia. Penelitian ini bertujuan untuk menginvestigasi: 1) perbedaan selective exposure atas konten berita palsu pada pendukung presiden petahana dan oposisi; 2) asosiasi antara kemampuan berpikir kritis dengan selective exposure pada partisan pendukung kandidat presiden. Repeated measures design digunakan sebagai desain eksperimen. Responden merupakan aktivis organisasi mahasiswa ekstra kampus yang memiliki ideologi politik tertentu dan memiliki preferensi pro terhadap petahana atau pro terhadap oposisi. Didapatkan 71 responden yang terdiri dari 34 pendukung petahana (Jokowi) dan 37 pendukung oposisi (Prabowo). Analisis data dilakukan dengan menggunakan independent t-test, paired sample t-test, dan analisis korelasi. Hasil analisis data mengindikasikan pendukung oposisi cenderung menunjukkan adanya selective exposure dengan mempercayai berita palsu atas lawan politik dibandingkan pada kubu pendukung petahana. Hal ini ditunjukkan dengan tendensi untuk percaya dan menyebarkan berita yang mendiskreditkan lawan politiknya dibandingkan dengan berita yang mendiskreditkan kubu yang didukungnya. Tidak terdapat asosiasi antara berpikir kritis dengan selective exposure pada partisan. Implikasi hasil ini adalah titik kritis pada debiasing bukan semata pada kemampuan berpikir analitik namun dimungkinkan pada kemampuan berpikir secara lebih terbuka.

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Section: Discussionmentioning

confidence: 99%

Partisan Selective Exposure to Fake News Content

Ardi¹

2019

Makara Hubs-Asia

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“…This would include instruction on how and why biases occur and debiasing strategies to mitigate them, but also practice with choice infrastructure creation so decision makers are not required to self-employ strategies to rise above their ingrained and subtle biases. How best to teach these courses and what kind of content, guidance, and practice to offer is an important question for future education research (for discussion on the possible virtues of "nudge" education, see Beaulac and Kenyon (2014); for discussion of how to achieve such institutional education changes see Henderson et al (2015)…”

Section: Box 1 Can Better Decision Making Be Taught?mentioning

confidence: 99%

How can geologic decision making under uncertainty be improved?

Wilson¹,

Bond²,

Shipley³

2019

Preprint

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Abstract. In the geosciences, recent attention has been paid to the influence of uncertainty on expert decision making. When making decisions under conditions of uncertainty, people tend to employ heuristics (rules of thumb) based on experience, relying on their prior knowledge and beliefs to intuitively guide choice. Over 50 years of decision making research in cognitive psychology demonstrates that heuristics can lead to less-than-optimal decisions, collectively referred to as biases. For example, a geologist who confidently interprets ambiguous data as representative of a familiar category form their research (e.g., strike slip faults for expert in extensional domains) is exhibiting the availability bias, which occurs when people make judgments based on what is most dominant or accessible in memory. Given the important social and commercial implications of many geoscience decisions, there is a need to develop effective interventions for removing or mitigating decision bias. In this paper, we summarize the key insights from decision making research about how to reduce bias and review the literature on debiasing strategies. First, we define an optimal decision, since improving decision making requires having a standard to work towards. Next, we discuss the cognitive mechanisms underlying decision biases and describe three biases that have been shown to influence geoscientists decision making (availability bias, framing bias, anchoring bias). Finally, we review existing debiasing strategies that have applicability in the geosciences, with special attention given to those strategies that make use of information technology and artificial intelligence (AI). We present two case studies illustrating different applications of intelligent systems for the debiasing of geoscientific decision making, where debiased decision making is an emergent property of the coordinated and integrated processing of human-AI collaborative teams.

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“…Because deliberative processing is constrained by the capacity of available cognitive resources, in situations in which resources are already limited (e.g., high-mental-effort tasks, fatigue, sleep deprivation) decision makers will be more likely to rely on heuristic responses, thus making them particularly susceptible to bias in such situations. Also, in general, humans act as cognitive misers (Böckenholt, 2012;Stanovich, 2009), meaning even when cognitive resources are available for deliberative processing we tend to rely on less effortful intuitive processes. Thus, broadly speaking, decision-making may be debiased by changing the environment or through training change the decision maker, so default heuristic responses lead to good decisions or so the application of deliberative processing is supported under conditions in which it is otherwise unlikely to be applied (Milkman et al, 2009).…”

Section: Origins Of Decision Biasesmentioning

confidence: 99%

How can geologic decision-making under uncertainty be improved?

2019

View full text Add to dashboard Cite

Abstract. In the geosciences, recent attention has been paid to the influence of uncertainty on expert decision-making. When making decisions under conditions of uncertainty, people tend to employ heuristics (rules of thumb) based on experience, relying on their prior knowledge and beliefs to intuitively guide choice. Over 50 years of decision-making research in cognitive psychology demonstrates that heuristics can lead to less-than-optimal decisions, collectively referred to as biases. For example, the availability bias occurs when people make judgments based on what is most dominant or accessible in memory; geoscientists who have spent the past several months studying strike-slip faults will have this terrain most readily available in their mind when interpreting new seismic data. Given the important social and commercial implications of many geoscience decisions, there is a need to develop effective interventions for removing or mitigating decision bias. In this paper, we outline the key insights from decision-making research about how to reduce bias and review the literature on debiasing strategies. First, we define an optimal decision, since improving decision-making requires having a standard to work towards. Next, we discuss the cognitive mechanisms underlying decision biases and describe three biases that have been shown to influence geoscientists' decision-making (availability bias, framing bias, anchoring bias). Finally, we review existing debiasing strategies that have applicability in the geosciences, with special attention given to strategies that make use of information technology and artificial intelligence (AI). We present two case studies illustrating different applications of intelligent systems for the debiasing of geoscientific decision-making, wherein debiased decision-making is an emergent property of the coordinated and integrated processing of human–AI collaborative teams.

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Critical Thinking Education and Debiasing (AILACT Essay Prize Winner 2013)

Cited by 51 publications

References 34 publications

Partisan Selective Exposure to Fake News Content

Partisan Selective Exposure to Fake News Content

How can geologic decision making under uncertainty be improved?

How can geologic decision-making under uncertainty be improved?

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