The 1st Workshop on Human-Centered Recommender Systems (HCRS) - Call for Papers

핵심 개념

The workshop aims to explore and advance the development of Human-Centered Recommender Systems (HCRS) that prioritize human needs, values, and capabilities, addressing challenges like the information cocoon effect and promoting ethical, user-centric, and socially responsible recommendations.

초록

This is a call for papers for the 1st Workshop on Human-Centered Recommender Systems (HCRS).

Workshop Focus

The workshop focuses on the development of HCRS that prioritize human needs and values in their design and operation. It aims to address challenges posed by traditional recommender systems, such as the information cocoon effect, privacy concerns, fairness issues, and lack of transparency.

Key Themes and Topics

The workshop invites submissions on a range of topics related to HCRS, including:

• Robustness: Addressing vulnerabilities and ensuring the reliability of recommender systems.
• Privacy: Protecting user data and mitigating privacy risks.
• Transparency: Making the reasoning behind recommendations clear and understandable to users.
• Fairness and Bias: Ensuring recommendations are equitable and free from unintended bias.
• Diversity: Promoting diverse content and recommendations to counteract filter bubbles.
• Ethics: Addressing the ethical implications of recommender systems and ensuring responsible use.
• Accountability: Establishing mechanisms for accountability and control over recommendations.
• Human-Computer Interaction Design: Creating user-friendly interfaces and interactions.
• Evaluation, Auditing, and Governance: Developing methods for evaluating, auditing, and governing HCRS.

Workshop Format

The half-day workshop will feature keynote talks by leading researchers, paper presentations, and a panel discussion on the future directions and challenges in the field of HCRS.

Target Audience

The workshop welcomes researchers, industry experts, and academics interested in advancing the field of recommender systems towards a more human-centered approach.

Submission Guidelines

• Papers should be formatted according to the ACM WWW 2025 template.
• Manuscripts can be 4-8 pages long, with unlimited pages for references.
• Submissions will undergo a double-blind review process.

Important Dates

• Submission Deadline: December 18, 2024
• Paper Acceptance Notification: January 13, 2025
• Camera-Ready Submission: February 2, 2025

The workshop aims to foster a collaborative environment for sharing insights and advancing the development of more ethical, user-centric, and socially responsible recommender systems.

통계

인용구

"HCRS refers to the creation of recommender systems that prioritize human needs, values, and capabilities at the core of their design and operation."
"HCRS not only addresses trustworthiness and responsibility but also actively involves users in the design and evaluation of recommender systems to ensure they align with users’ goals and capabilities."

핵심 통찰 요약

The 1st Workshop on Human-Centered Recommender Systems

by Kaike Zhang,... 게시일 arxiv.org 11-25-2024

https://arxiv.org/pdf/2411.14760.pdf

더 깊은 질문

How can the principles of HCRS be applied to other areas of artificial intelligence and machine learning beyond recommender systems?

The principles of Human-Centered Recommender Systems (HCRS), emphasizing human needs, values, and capabilities, have broad applicability across various domains of artificial intelligence (AI) and machine learning (ML) beyond recommender systems. Here's how:

Personalized Learning Environments: HCRS principles can be used to develop AI-powered learning platforms that adapt to individual learning styles, preferences, and pace. This includes tailoring content, providing personalized feedback, and suggesting relevant learning resources, ultimately enhancing user engagement and knowledge acquisition.
Healthcare and Well-being: HCRS can contribute to developing AI-driven healthcare applications that prioritize patient values and needs. This includes designing systems for personalized treatment recommendations, mental health support, and patient education, ensuring sensitivity, transparency, and trust.
Human-Robot Interaction: In robotics, HCRS principles are crucial for designing robots that interact with humans in a safe, intuitive, and ethical manner. This involves incorporating human values into robot decision-making processes, ensuring transparency in robot actions, and fostering trust between humans and robots.
AI-Assisted Creativity and Design: HCRS can guide the development of AI tools that augment human creativity in fields like art, music, and design. These tools should be designed to empower users, respect their creative vision, and provide meaningful collaboration opportunities, rather than replacing human creativity.
Fair and Ethical AI Systems: The emphasis on fairness, accountability, and transparency in HCRS is directly applicable to broader efforts in developing ethical AI systems. By incorporating human values and addressing potential biases in data and algorithms, we can strive for AI systems that are fair, unbiased, and promote social good.
In essence, the core principles of HCRS—prioritizing human needs, values, and capabilities—provide a valuable framework for developing AI and ML systems across various domains, ensuring that these technologies are beneficial, trustworthy, and aligned with human well-being.

Could an excessive focus on human values and preferences in recommender systems inadvertently limit users' exposure to diverse perspectives and hinder serendipitous discovery?

Yes, an excessive focus on human values and preferences in recommender systems, while seemingly user-centric, can lead to an "echo chamber" or "filter bubble" effect. This occurs when algorithms, in their quest to personalize, predominantly suggest content aligning with a user's existing beliefs and preferences, inadvertently limiting exposure to diverse perspectives and hindering serendipitous discovery.
Here's how this happens and its implications:

Reinforcement of Existing Biases: By primarily recommending content that confirms pre-existing views, recommender systems can reinforce biases and limit a user's understanding of different viewpoints. This can lead to intellectual isolation and hinder the development of well-rounded perspectives.
Missed Opportunities for Exploration: Serendipitous discovery, the joy of encountering something unexpected and valuable, is often stifled in overly personalized environments. Limiting recommendations to a narrow band of preferences can prevent users from discovering new interests, hobbies, or information that could broaden their horizons.
Homogenization of Experiences: While personalization is valuable, an excessive focus on individual preferences can lead to a homogenization of online experiences. This can stifle cultural exchange, limit opportunities for intellectual growth, and potentially lead to societal fragmentation.
To mitigate these risks, developers of human-centered recommender systems should consider:

Diversity-Promoting Mechanisms: Implementing algorithms that actively suggest content outside a user's typical preferences, ensuring exposure to a wider range of viewpoints and information.
Transparency and User Control: Providing users with transparency into how recommendations are generated and offering controls to adjust the level of personalization and diversity.
Content Curation and Editorial Input: Incorporating human curation and editorial input to ensure a balance between personalized recommendations and exposure to diverse, high-quality content.
Balancing personalization with diversity is crucial for creating truly human-centered recommender systems that cater to both individual preferences and the need for broader exposure and intellectual exploration.

What role should government regulation and public policy play in ensuring the ethical development and deployment of human-centered recommender systems?

Government regulation and public policy play a crucial role in establishing a framework for the ethical development and deployment of human-centered recommender systems (HCRS). While encouraging innovation, it's essential to mitigate potential risks and ensure these systems benefit individuals and society as a whole. Here are some key areas for policy intervention:

Data Privacy and Security: Implement regulations that protect user data collected and used by recommender systems. This includes ensuring data transparency, user consent, and secure data storage practices to prevent misuse or breaches.
Algorithmic Transparency and Explainability: Promote policies that require developers to provide clear explanations of how their algorithms work, particularly regarding data usage, recommendation logic, and potential biases. This transparency allows for better scrutiny, accountability, and user trust.
Fairness and Non-Discrimination: Establish guidelines and regulations that prohibit discriminatory practices in recommender systems. This includes ensuring algorithms do not perpetuate biases based on factors like race, gender, religion, or socioeconomic status, promoting equal opportunity and access to information.
Mitigation of Filter Bubbles and Echo Chambers: Encourage policies that address the issue of filter bubbles and echo chambers. This could involve requiring platforms to provide users with options to diversify their recommendations, promoting exposure to a wider range of viewpoints and information.
User Control and Empowerment: Implement regulations that empower users with greater control over their recommendations. This includes providing options to adjust personalization levels, understand recommendation rationale, and opt-out of data collection or specific recommendation features.
Education and Public Awareness: Foster public awareness campaigns to educate users about the capabilities, limitations, and potential impact of recommender systems. This empowers individuals to make informed decisions about their online interactions and advocate for ethical development practices.
Finding the right balance between fostering innovation and implementing appropriate regulations is crucial. Collaboration between policymakers, researchers, industry leaders, and advocacy groups is essential to develop effective and ethical guidelines for human-centered recommender systems that benefit both individuals and society.