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. …”

Preference for the EIA vs. ETA 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. …”

Charge-Transfer-Driven Electrical Conductivity in Single Crystals of Assembled Triphenylamine Bis-urea Macrocycles by Fahidat A. Gbadamosi (22123930)

Subjects:

Charge-Transfer-Driven Electrical Conductivity in Single Crystals of Assembled Triphenylamine Bis-urea Macrocycles by Fahidat A. Gbadamosi (22123930)

Subjects:

Charge-Transfer-Driven Electrical Conductivity in Single Crystals of Assembled Triphenylamine Bis-urea Macrocycles by Fahidat A. Gbadamosi (22123930)

Subjects:

Charge-Transfer-Driven Electrical Conductivity in Single Crystals of Assembled Triphenylamine Bis-urea Macrocycles by Fahidat A. Gbadamosi (22123930)

Subjects:

Charge-Transfer-Driven Electrical Conductivity in Single Crystals of Assembled Triphenylamine Bis-urea Macrocycles by Fahidat A. Gbadamosi (22123930)

Subjects:

Charge-Transfer-Driven Electrical Conductivity in Single Crystals of Assembled Triphenylamine Bis-urea Macrocycles by Fahidat A. Gbadamosi (22123930)

Subjects:

Comparison of the cohesion ranges of different food categories under IDDSI levels. by Muxi Chen (9294270)

Subjects:

Relationships between cohesiveness and IDDSI levels. by Muxi Chen (9294270)

Subjects:

Relationships between adhesiveness and IDDSI levels. by Muxi Chen (9294270)

Subjects:

Comparison of adhesiveness ranges for different food categories under IDDSI levels. by Muxi Chen (9294270)

Subjects:

Cohesiveness changes across IDDSI levels and food categories. by Muxi Chen (9294270)

Subjects:

Multiple linear regression analysis results. by Muxi Chen (9294270)

Subjects: