Evaluation of risk model. by Yajia Chen (20685780)

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Baseline characteristics and oxidative profiling. by Summan Thahiem (20988319)

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Supplementary data: Flowchart of the study. by Suphanida Kaewkamchai (19762172)

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Distinct and shared gene expression patterns among leukemia cell lines following EF-24 treatment. by Ajeet P. Singh (22298432)

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The expression profile of lipogenic genes, oxidative stress mediators as well as UPR^ER and mitochondrial stress markers. by Summan Thahiem (20988319)

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Literature comparison. by Saravanan Manoharan (21273304)

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Flow chart of proposed system. by Saravanan Manoharan (21273304)

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Literature review. by Saravanan Manoharan (21273304)

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IMU data and video synchronization. by Saravanan Manoharan (21273304)

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Confusion matrix-punch classification. by Saravanan Manoharan (21273304)

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Experimental design of this study. by Renya Kawakami (20469088)

“…In fact, naive old males exhibited significantly higher paternity success compared with old males who had previously mated. …”

All relevant data of this study. by Renya Kawakami (20469088)

“…In fact, naive old males exhibited significantly higher paternity success compared with old males who had previously mated. …”

Volcano map of DAPs. by Caijin Chen (3107787)

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Preference for the EIA – conjoint results. by Mehdi Mourali (10170245)

“…When are individuals more likely to support equal treatment algorithms (ETAs), characterized by higher predictive accuracy, and when do they prefer equal impact algorithms (EIAs) that reduce performance gaps between groups? A randomized conjoint experiment and a follow-up choice experiment revealed that support for the EIAs decreased sharply as their accuracy gap grew, although impact parity was prioritized more when ETAs produced large outcome discrepancies. …”

Marginal means – Pooled across scenarios. by Mehdi Mourali (10170245)

“…When are individuals more likely to support equal treatment algorithms (ETAs), characterized by higher predictive accuracy, and when do they prefer equal impact algorithms (EIAs) that reduce performance gaps between groups? A randomized conjoint experiment and a follow-up choice experiment revealed that support for the EIAs decreased sharply as their accuracy gap grew, although impact parity was prioritized more when ETAs produced large outcome discrepancies. …”

Sample attribute table. by Mehdi Mourali (10170245)

“…When are individuals more likely to support equal treatment algorithms (ETAs), characterized by higher predictive accuracy, and when do they prefer equal impact algorithms (EIAs) that reduce performance gaps between groups? A randomized conjoint experiment and a follow-up choice experiment revealed that support for the EIAs decreased sharply as their accuracy gap grew, although impact parity was prioritized more when ETAs produced large outcome discrepancies. …”

Subgroup analysis – Political affiliation. by Mehdi Mourali (10170245)

“…When are individuals more likely to support equal treatment algorithms (ETAs), characterized by higher predictive accuracy, and when do they prefer equal impact algorithms (EIAs) that reduce performance gaps between groups? A randomized conjoint experiment and a follow-up choice experiment revealed that support for the EIAs decreased sharply as their accuracy gap grew, although impact parity was prioritized more when ETAs produced large outcome discrepancies. …”

Sample scenario description. by Mehdi Mourali (10170245)

“…When are individuals more likely to support equal treatment algorithms (ETAs), characterized by higher predictive accuracy, and when do they prefer equal impact algorithms (EIAs) that reduce performance gaps between groups? A randomized conjoint experiment and a follow-up choice experiment revealed that support for the EIAs decreased sharply as their accuracy gap grew, although impact parity was prioritized more when ETAs produced large outcome discrepancies. …”

AMCEs – Pooled across scenarios. by Mehdi Mourali (10170245)

“…When are individuals more likely to support equal treatment algorithms (ETAs), characterized by higher predictive accuracy, and when do they prefer equal impact algorithms (EIAs) that reduce performance gaps between groups? A randomized conjoint experiment and a follow-up choice experiment revealed that support for the EIAs decreased sharply as their accuracy gap grew, although impact parity was prioritized more when ETAs produced large outcome discrepancies. …”

Methodological flowchart. by Mehdi Mourali (10170245)

“…When are individuals more likely to support equal treatment algorithms (ETAs), characterized by higher predictive accuracy, and when do they prefer equal impact algorithms (EIAs) that reduce performance gaps between groups? A randomized conjoint experiment and a follow-up choice experiment revealed that support for the EIAs decreased sharply as their accuracy gap grew, although impact parity was prioritized more when ETAs produced large outcome discrepancies. …”