Illuminations (a) and attenuation rates (b) of the RHL along the axis of y = 0 m at different angles. by Fei Li (143236)

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Illuminations (a) and attenuation rates (b) of the CL along the axis of y = 0 m at different angles. by Fei Li (143236)

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Distances (m) from the origin for illumination curves of 1000 and 10 lx of three module types at different angles. by Fei Li (143236)

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Illumination distributions (a-c) and variations in illuminance values along the x-axis (d-f) of the CL at different angles. by Fei Li (143236)

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Illumination distributions (a-c) and variations in illuminance values along the x-axis (d-f) of the RHL at different angles. by Fei Li (143236)

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Design of the lamp beads in the virtual circular module (unit: cm). by Fei Li (143236)

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The maximum differences in positive illuminance (lx) and their distances to the origin (m) among various configurations at different angles. by Fei Li (143236)

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Schematic diagram of the spatial relationships between the module and the illuminated area. by Fei Li (143236)

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Illumination distributions (a-c) and variations in illuminance values along the <i>x</i>-axis (d-f) of the RVL at different angles. by Fei Li (143236)

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The maximum illuminance values (lx) and distances to the origin (m) of various configurations at different angles. by Fei Li (143236)

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The differences in illumination (a-c) and their three-dimensional distributions (d-f) between RVL and RHL at different angles. by Fei Li (143236)

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Parameters of the rectangular LED module (unit: cm). by Fei Li (143236)

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The fitted illumination data of LED lamp at different angles. by Fei Li (143236)

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The differences in illumination (a-c) and their three-dimensional distributions (d-f) between RVL and CL at different angles. by Fei Li (143236)

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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. …”