Trustworthy Recommender Systems

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This document discusses future directions for deep recommender systems across five key dimensions: privacy, explainability, fairness, robustness, and accountability. It notes that recommender systems rely heavily on private user data and explores how to protect privacy while leveraging data advantages. Explainability is important to address issues around transparency, trust, and debugging models. Fairness aims to avoid discrimination and ensure equitable decision making. Robustness examines adversarial attacks and fake user profiles. Accountability determines responsibility across system designers, decision makers, deployers, and auditors. Interactions among these dimensions must also be considered to guide future work in developing more trustworthy recommender systems.

Technologie

Deep Recommendations:
Future Directions
1
Data Science and Engineering Lab
Tutorial website: https://advanced-recommender-systems.github.io/ijcai2021-tutorial/
Wenqi Fan
The Hong Kong Polytechnic University
https://wenqifan03.github.io, wenqifan@polyu.edu.hk

Recommender System is Everywhere
2
Business Healthcare
Entertainment Education

Trustworthy Recommender Systems
3
Trustworthy AI: A Computational Perspective, ArXiv: 2107.06641, 2021.
Tutorial: https://sites.google.com/msu.edu/trustworthy-ai/

Privacy Issue
4
q The success of recommender systems
heavily relies on data that might
contain private and sensitive
information.
q Can we still take the advantages of
data while effectively protecting the
privacy?

How recommender systems work?
Explainability Issue
5
Learning
Process
Training
Data
Learned
Function
Output
Today
• Why did you do that?
• Why not something else?
• When do you succeed?
• When do you fail?
• When can I trust you?
• How do I correct an error?
User with
a Task
Black-box
RecSys
©University Of Toronto
User-item Rating Matrix !
5 4
…
5
…
5
…
5 1
2 5
!
users
" items (movies)
Lily
Lala
Peter
David
…
Spider
Man Iron Man
Toy
Story Minions
Captain
America
?
…
? ?
? ? ? ?
…
? ?
…
? ? ?
…
#
$!"

Black-box AI creates confusion and doubt
Explainability Issue
6
User with
a Task
IT & Operations
Can I trust our
system’s decisions?
Business Owner
Data Scientists
Are these system
decisions fair?
Internal Audit, Regulators
Customer Support
How do I answer this
customer complaint?
How do I monitor and
debug this model?
Is this the best model
that can be built?
Output
The Need for Explainable Recommendation
From Black-box
to “Transparent”
Yongfeng Zhang, et.al, Explainable Recommendation: A Survey and New Perspectives, 2020.

Explainability Issue
7
Training
Data
New
Learning
Process
Explainable
Model
Explanation
Interface
Tomorrow
• I understand why
• I understand why not
• I know when you’ll succeed
• I know when you’ll fail
• I know when to trust you
• I know why you erred
User with
a Task
©University Of Toronto
©University Of Toronto
User-item Rating Matrix !
5 4
…
5
…
5
…
5 1
2 5
!
users
" items (movies)
Lily
Lala
Peter
David
…
Spider
Man Iron Man
Toy
Story Minions
Captain
America
?
…
? ?
? ? ? ?
…
? ?
…
? ? ?
…
#
$!"

Discrimination & Fairness Issue
8
Job recommendation
(Lambrecht et al., 2019)
Lambrecht, et al. "Algorithmic bias? An empirical study of apparent gender-based discrimination in the display of STEM career ads." 2019.
Bias and Debias in Recommender System: A Survey and Future Directions, 2021.

§ A recommender system should avoid discriminatory behaviors in
human-machine interaction.
§ A recommender system should ensure fairness in decision-making.
Non-discrimination & Fairness

Safety & Robustness Issue
10
Adversary
(fake user profiles)

§ Accountability
Auditability & Accountability
11
Accountability: A clear responsibility distribution, which focuses on who
should take the responsibility for what impact of recommender systems.
Who is to
blame?
Violent movie

§ Five roles in Recommender Systems
Auditability & Accountability
12
System
Designers
Decision
Makers
System
Deployers
System
Auditors
End Users
It is necessary to determine the roles and the corresponding responsibility of
different parties in the function of a recommender system.

Interactions Among Different Dimensions
13
How do these five dimensions influence each other?
There exist both accordance and the conflicts among the five dimensions.

A Survey on The Computational Perspective
14
https://arxiv.org/abs/2107.06641
Tutorial website: https://sites.google.com/msu.edu/trustworthy-ai/home

15
Acknowledgements
Thanks for this great opportunity offered by IJCAI 2021 and our funding support
from MSU, AERA, NSF, ARO, DARPA,NSFC, GRF, and industrial collaborators.

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Discord is a free app offering voice, video, and text chat functionalities, primarily catering to the gaming community. It serves as a hub for users to create and join servers tailored to their interests. Discord’s ecosystem comprises servers, each functioning as a distinct online community with its own channels dedicated to specific topics or activities. Users can engage in text-based discussions, voice calls, or video chats within these channels. Understanding Discord Servers Discord servers are virtual spaces where users congregate to interact, share content, and build communities. Servers may revolve around gaming, hobbies, interests, or fandoms, providing a platform for like-minded individuals to connect. Communication Features Discord offers a range of communication tools, including text channels for messaging, voice channels for real-time audio conversations, and video channels for face-to-face interactions. These features facilitate seamless communication and collaboration. What Does NSFW Mean? The acronym NSFW stands for “Not Safe For Work,” indicating content that may be inappropriate for professional or public settings. NSFW Content NSFW content encompasses material that is sexually explicit, violent, or otherwise graphic in nature. It often includes nudity, profanity, or depictions of sensitive topics.

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Imagine a world where information flows as swiftly as thought itself, making decision-making as fluid as the data driving it. Every moment is critical, and the right tools can significantly boost your organization’s performance. The power of real-time data automation through FME can turn this vision into reality. Aimed at professionals eager to leverage real-time data for enhanced decision-making and efficiency, this webinar will cover the essentials of real-time data and its significance. We’ll explore: FME’s role in real-time event processing, from data intake and analysis to transformation and reporting An overview of leveraging streams vs. automations FME’s impact across various industries highlighted by real-life case studies Live demonstrations on setting up FME workflows for real-time data Practical advice on getting started, best practices, and tips for effective implementation Join us to enhance your skills in real-time data automation with FME, and take your operational capabilities to the next level.

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As privacy and data protection regulations evolve rapidly, organizations operating in multiple jurisdictions face mounting challenges to ensure compliance and safeguard customer data. With state-specific privacy laws coming up in multiple states this year, it is essential to understand what their unique data protection regulations will require clearly. How will data privacy evolve in the US in 2024? How to stay compliant? Our panellists will guide you through the intricacies of these states' specific data privacy laws, clarifying complex legal frameworks and compliance requirements. This webinar will review: - The essential aspects of each state's privacy landscape and the latest updates - Common compliance challenges faced by organizations operating in multiple states and best practices to achieve regulatory adherence - Valuable insights into potential changes to existing regulations and prepare your organization for the evolving landscape

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Building Digital Trust in a Digital Economy Veronica Tan, Director - Cyber Security Agency of Singapore Apidays Singapore 2024: Connecting Customers, Business and Technology (April 17 & 18, 2024) ------ Check out our conferences at https://www.apidays.global/ Do you want to sponsor or talk at one of our conferences? https://apidays.typeform.com/to/ILJeAaV8 Learn more on APIscene, the global media made by the community for the community: https://www.apiscene.io Explore the API ecosystem with the API Landscape: https://apilandscape.apiscene.io/

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In an era where artificial intelligence (AI) stands at the forefront of business innovation, Information Architecture (IA) is at the core of functionality. See “There’s No AI Without IA” – (from 2016 but even more relevant today) Understanding and leveraging how Information Architecture (IA) supports AI synergies between knowledge engineering and prompt engineering is critical for senior leaders looking to successfully deploy AI for internal and externally facing knowledge processes. This webinar be a high-level overview of the methodologies that can elevate AI-driven knowledge processes supporting both employees and customers. Core Insights Include: Strategic Knowledge Engineering: Delve into how structuring AI's knowledge base is required to prevent hallucinations, enable contextual retrieval of accurate information. This will include discussion of gold standard libraries of use cases support testing various LLMs and structures and configurations of knowledge base. Precision in Prompt Engineering: Learn the art of crafting prompts that direct AI to deliver targeted, relevant responses, thereby optimizing customer experiences and business outcomes. Unified Approach for Enhanced AI Performance: Explore the intersection of knowledge and prompt engineering to develop AI systems that are not only more responsive but also aligned with overarching business strategies. Guiding Principles for Implementation: Equip yourself with best practices, ethical guidelines, and strategic considerations for embedding these technologies into your business ecosystem effectively. This webinar is designed to empower business and technology leaders with the knowledge to harness the full potential of AI, ensuring their organizations not only keep pace with digital transformation but lead the charge. Join us to map a roadmap to fully leverage Information Architecture (IA) and AI chart a course towards a future where AI is a key pillar of strategic innovation and business success.

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In this session, we will delve into strategic approaches for optimizing knowledge management within Microsoft 365, amidst the evolving landscape of Copilot. From leveraging automatic metadata classification and permission governance with SharePoint Premium, to unlocking Viva Engage for the cultivation of knowledge and communities, you will gain actionable insights to bolster your organization's knowledge-sharing initiatives. In this session, we will also explore how to facilitate solutions to enable your employees to find answers and expertise within Microsoft 365. You will leave equipped with practical techniques and a deeper understanding of how there is more to effective knowledge management than just enabling Copilot, but building actual solutions to prepare the knowledge that Copilot and your employees can use.

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Trustworthy Recommender Systems

1. Deep Recommendations: Future Directions 1 Data Science and Engineering Lab Tutorial website: https://advanced-recommender-systems.github.io/ijcai2021-tutorial/ Wenqi Fan The Hong Kong Polytechnic University https://wenqifan03.github.io, wenqifan@polyu.edu.hk

2. Recommender System is Everywhere 2 Business Healthcare Entertainment Education

3. Trustworthy Recommender Systems 3 Trustworthy AI: A Computational Perspective, ArXiv: 2107.06641, 2021. Tutorial: https://sites.google.com/msu.edu/trustworthy-ai/

4. Privacy Issue 4 q The success of recommender systems heavily relies on data that might contain private and sensitive information. q Can we still take the advantages of data while effectively protecting the privacy?

5. How recommender systems work? Explainability Issue 5 Learning Process Training Data Learned Function Output Today • Why did you do that? • Why not something else? • When do you succeed? • When do you fail? • When can I trust you? • How do I correct an error? User with a Task Black-box RecSys ©University Of Toronto User-item Rating Matrix ! 5 4 … 5 … 5 … 5 1 2 5 ! users " items (movies) Lily Lala Peter David … Spider Man Iron Man Toy Story Minions Captain America ? … ? ? ? ? ? ? … ? ? … ? ? ? … # $!"

6. Black-box AI creates confusion and doubt Explainability Issue 6 User with a Task IT & Operations Can I trust our system’s decisions? Business Owner Data Scientists Are these system decisions fair? Internal Audit, Regulators Customer Support How do I answer this customer complaint? How do I monitor and debug this model? Is this the best model that can be built? Output The Need for Explainable Recommendation From Black-box to “Transparent” Yongfeng Zhang, et.al, Explainable Recommendation: A Survey and New Perspectives, 2020.

7. Explainability Issue 7 Training Data New Learning Process Explainable Model Explanation Interface Tomorrow • I understand why • I understand why not • I know when you’ll succeed • I know when you’ll fail • I know when to trust you • I know why you erred User with a Task ©University Of Toronto ©University Of Toronto User-item Rating Matrix ! 5 4 … 5 … 5 … 5 1 2 5 ! users " items (movies) Lily Lala Peter David … Spider Man Iron Man Toy Story Minions Captain America ? … ? ? ? ? ? ? … ? ? … ? ? ? … # $!"

8. Discrimination & Fairness Issue 8 Job recommendation (Lambrecht et al., 2019) Lambrecht, et al. "Algorithmic bias? An empirical study of apparent gender-based discrimination in the display of STEM career ads." 2019. Bias and Debias in Recommender System: A Survey and Future Directions, 2021.

9. § A recommender system should avoid discriminatory behaviors in human-machine interaction. § A recommender system should ensure fairness in decision-making. Non-discrimination & Fairness

10. Safety & Robustness Issue 10 Adversary (fake user profiles)

11. § Accountability Auditability & Accountability 11 Accountability: A clear responsibility distribution, which focuses on who should take the responsibility for what impact of recommender systems. Who is to blame? Violent movie

12. § Five roles in Recommender Systems Auditability & Accountability 12 System Designers Decision Makers System Deployers System Auditors End Users It is necessary to determine the roles and the corresponding responsibility of different parties in the function of a recommender system.

13. Interactions Among Different Dimensions 13 How do these five dimensions influence each other? There exist both accordance and the conflicts among the five dimensions.

14. A Survey on The Computational Perspective 14 https://arxiv.org/abs/2107.06641 Tutorial website: https://sites.google.com/msu.edu/trustworthy-ai/home

15. 15 Acknowledgements Thanks for this great opportunity offered by IJCAI 2021 and our funding support from MSU, AERA, NSF, ARO, DARPA,NSFC, GRF, and industrial collaborators.

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