6 papers
Driver Fatigue Prediction using Randomly Activated Neural Networks for Smart Ridesharing Platforms
Sree Pooja Akula, Mukund Telukunta, Venkata Sriram Siddhardh Nadendla
Drivers in ridesharing platforms exhibit cognitive atrophy and fatigue as they accept ride offers along the day, which can have a significant impact on the overall efficiency of th…
Learning Social Fairness Preferences from Non-Expert Stakeholder Opinions in Kidney Placement
Mukund Telukunta, Sukruth Rao, Gabriella Stickney +2
Modern kidney placement incorporates several intelligent recommendation systems which exhibit social discrimination due to biases inherited from training data. Although initial att…
Towards Inclusive Fairness Evaluation via Eliciting Disagreement Feedback from Non-Expert Stakeholders
Mukund Telukunta, Venkata Sriram Siddhardh Nadendla
Traditional algorithmic fairness notions rely on label feedback, which can only be elicited from expert critics. However, in most practical applications, several non-expert stakeho…
On Learning and Enforcing Latent Assessment Models using Binary Feedback from Human Auditors Regarding Black-Box Classifiers
Mukund Telukunta, Venkata Sriram Siddhardh Nadendla
Algorithmic fairness literature presents numerous mathematical notions and metrics, and also points to a tradeoff between them while satisficing some or all of them simultaneously.…
Non-Comparative Fairness for Human-Auditing and Its Relation to Traditional Fairness Notions
Mukund Telukunta, Venkata Sriram Siddhardh Nadendla
Bias evaluation in machine-learning based services (MLS) based on traditional algorithmic fairness notions that rely on comparative principles is practically difficult, making it n…
On the Identification of Fair Auditors to Evaluate Recommender Systems based on a Novel Non-Comparative Fairness Notion
Mukund Telukunta, Venkata Sriram Siddhardh Nadendla
Decision-support systems are information systems that offer support to people's decisions in various applications such as judiciary, real-estate and banking sectors. Lately, these…