Hugo Berard

2025-08-13

ArXiv (preprint)

Co-Producing AI: Toward an Augmented, Participatory Lifecycle

Rashid A. Mushkani

Toumadher Ammar

Cassandre Chatonnier

Despite efforts to mitigate the inherent risks and biases of artificial intelligence (AI) algorithms, these algorithms can disproportionatel… (see more)y impact culturally marginalized groups. A range of approaches has been proposed to address or reduce these risks, including the development of ethical guidelines and principles for responsible AI, as well as technical solutions that promote algorithmic fairness. Drawing on design justice, expansive learning theory, and recent empirical work on participatory AI, we argue that mitigating these harms requires a fundamental re-architecture of the AI production pipeline. This re-design should center co-production, diversity, equity, inclusion (DEI), and multidisciplinary collaboration. We introduce an augmented AI lifecycle consisting of five interconnected phases: co-framing, co-design, co-implementation, co-deployment, and co-maintenance. The lifecycle is informed by four multidisciplinary workshops and grounded in themes of distributed authority and iterative knowledge exchange. Finally, we relate the proposed lifecycle to several leading ethical frameworks and outline key research questions that remain for scaling participatory governance.

2025-07-31

ArXiv (preprint)

Public perceptions of Montréal's streets: Implications for inclusive public space making and management

Rashid A. Mushkani

Toumadher Ammar

2025-07-01

Journal of Urban Management (published)

Negotiative Alignment: Embracing Disagreement to Achieve Fairer Outcomes -- Insights from Urban Studies

Rashid A. Mushkani

2025-03-16

ArXiv (preprint)

LIVS: A Pluralistic Alignment Dataset for Inclusive Public Spaces

Rashid A. Mushkani

Shravan Nayak

Allison Cohen

Hadrien Bertrand

2025-03-01

arXiv (published)

Negotiative Alignment: Embracing Disagreement to Achieve Fairer Outcomes -- Insights from Urban Studies

Rashid A. Mushkani

2025-03-01

arXiv (published)

LIVS: A Pluralistic Alignment Dataset for Inclusive Public Spaces

Rashid A. Mushkani

Shravan Nayak

Allison Cohen

Hadrien Bertrand

We introduce the Local Intersectional Visual Spaces (LIVS) dataset, a benchmark for multi-criteria alignment of text-to-image (T2I) models i… (see more)n inclusive urban planning. Developed through a two-year participatory process with 30 community organizations, LIVS encodes diverse spatial preferences across 634 initial concepts, consolidated into six core criteria: Accessibility, Safety, Comfort, Invitingness, Inclusivity, and Diversity, through 37,710 pairwise comparisons. Using Direct Preference Optimization (DPO) to fine-tune Stable Diffusion XL, we observed a measurable increase in alignment with community preferences, though a significant proportion of neutral ratings highlights the complexity of modeling intersectional needs. Additionally, as annotation volume increases, accuracy shifts further toward the DPO-tuned model, suggesting that larger-scale preference data enhances fine-tuning effectiveness. LIVS underscores the necessity of integrating context-specific, stakeholder-driven criteria into generative modeling and provides a resource for evaluating AI alignment methodologies across diverse socio-spatial contexts.

2025-02-27

ArXiv (preprint)

AI-EDI-SPACE: A Co-designed Dataset for Evaluating the Quality of Public Spaces

S. Gowaikar

Rashid A. Mushkani

Emmanuel Beaudry Marchand

Toumadher Ammar

Advancements in AI heavily rely on large-scale datasets meticulously curated and annotated for training. However, concerns persist regarding… (see more) the transparency and context of data collection methodologies, especially when sourced through crowdsourcing platforms. Crowdsourcing often employs low-wage workers with poor working conditions and lacks consideration for the representativeness of annotators, leading to algorithms that fail to represent diverse views and perpetuate biases against certain groups. To address these limitations, we propose a methodology involving a co-design model that actively engages stakeholders at key stages, integrating principles of Equity, Diversity, and Inclusion (EDI) to ensure diverse viewpoints. We apply this methodology to develop a dataset and AI model for evaluating public space quality using street view images, demonstrating its effectiveness in capturing diverse perspectives and fostering higher-quality data.

2024-11-01

arXiv (published)

AI-EDI-SPACE: A Co-designed Dataset for Evaluating the Quality of Public Spaces

S. Gowaikar

Rashid A. Mushkani

Emmanuel Beaudry Marchand

Toumadher Ammar

Advancements in AI heavily rely on large-scale datasets meticulously curated and annotated for training. However, concerns persist regarding… (see more) the transparency and context of data collection methodologies, especially when sourced through crowdsourcing platforms. Crowdsourcing often employs low-wage workers with poor working conditions and lacks consideration for the representativeness of annotators, leading to algorithms that fail to represent diverse views and perpetuate biases against certain groups. To address these limitations, we propose a methodology involving a co-design model that actively engages stakeholders at key stages, integrating principles of Equity, Diversity, and Inclusion (EDI) to ensure diverse viewpoints. We apply this methodology to develop a dataset and AI model for evaluating public space quality using street view images, demonstrating its effectiveness in capturing diverse perspectives and fostering higher-quality data.

2024-11-01

ArXiv (preprint)

From Efficiency to Equity: Measuring Fairness in Preference Learning

S. Gowaikar

Rashid A. Mushkani

As AI systems, particularly generative models, increasingly influence decision-making, ensuring that they are able to fairly represent diver… (see more)se human preferences becomes crucial. This paper introduces a novel framework for evaluating epistemic fairness in preference learning models inspired by economic theories of inequality and Rawlsian justice. We propose metrics adapted from the Gini Coefficient, Atkinson Index, and Kuznets Ratio to quantify fairness in these models. We validate our approach using two datasets: a custom visual preference dataset (AI-EDI-Space) and the Jester Jokes dataset. Our analysis reveals variations in model performance across users, highlighting potential epistemic injustices. We explore pre-processing and in-processing techniques to mitigate these inequalities, demonstrating a complex relationship between model efficiency and fairness. This work contributes to AI ethics by providing a framework for evaluating and improving epistemic fairness in preference learning models, offering insights for developing more inclusive AI systems in contexts where diverse human preferences are crucial.

2024-10-24

ArXiv (preprint)

From Efficiency to Equity: Measuring Fairness in Preference Learning

S. Gowaikar

Rashid A. Mushkani

As AI systems, particularly generative models, increasingly influence decision-making, ensuring that they are able to fairly represent diver… (see more)se human preferences becomes crucial. This paper introduces a novel framework for evaluating epistemic fairness in preference learning models inspired by economic theories of inequality and Rawlsian justice. We propose metrics adapted from the Gini Coefficient, Atkinson Index, and Kuznets Ratio to quantify fairness in these models. We validate our approach using two datasets: a custom visual preference dataset (AI-EDI-Space) and the Jester Jokes dataset. Our analysis reveals variations in model performance across users, highlighting potential epistemic injustices. We explore pre-processing and in-processing techniques to mitigate these inequalities, demonstrating a complex relationship between model efficiency and fairness. This work contributes to AI ethics by providing a framework for evaluating and improving epistemic fairness in preference learning models, offering insights for developing more inclusive AI systems in contexts where diverse human preferences are crucial.

2024-10-24

ArXiv (preprint)

Evaluation algorithmique inclusive de la qualité des espaces publics

Toumadher Ammar

Rashid Ahmad Mushkani

Sarah Tannir

2024-01-01

SHS Web of Conferences (published)