The document outlines a tutorial on trustworthy music information retrieval (MIR), emphasizing the ethical and legal considerations necessary for AI systems applied to music. It discusses the importance of human agency, technical robustness, privacy, transparency, diversity, societal impact, and accountability throughout the AI system's lifecycle. Various evaluation strategies for MIR systems that account for multiple stakeholder perspectives and user-centered approaches are also highlighted.

1. Trustworthy MIR
ISMIR 2022 Tutorial
Christine Bauer, Andrés Ferraro,
Emilia Gómez, Lorenzo Porcaro
1

2. 2
Christine Bauer
Utrecht University,
The Netherlands
Andrés Ferraro
McGill University &
Mila, Canada*
Emilia Gómez,
Music Technology
Group (UPF) &
Joint Research
Center (EC), Spain
Lorenzo Porcaro
Music Technology
Group (UPF) &
Joint Research
Center (EC), Italy
* Currently at Pandora-SiriusXM

3. Outline
3
➔ Part 1 - Trustworthy MIR
◆ Trustworthy AI and its application to Music IR (Emilia, 30’)
< SHORT BREAK 10’>
◆ Evaluation (Christine, 30’)
< LONG BREAK 20’>
➔ Part 2 - Values in practice
◆ Diversity (Lorenzo, 30’)
< SHORT BREAK 10’>
◆ Fairness (Andrés, 30’)
➔ Wrap up (10’)

4. Part I
Trustworthy AI and its application to
Music IR
4

5. 5
Trustworthy /ˈtrʌstˌwɚði/
: able to be relied on to do or provide what is needed
or right; deserving of trust; worthy of confidence.

6. System-oriented
Extraction and inference
of meaningful
information from music.
Socio-technical system
Interaction with the social
system, e.g. business
processes, organizations,
society (law, culture).
Markus Schedl, Emilia Gómez and Julián Urbano (2014), "Music Information Retrieval:
Recent Developments and Applications", Foundations and Trends in Information Retrieval:
Vol. 8: No. 2-3, pp 127-261. http://dx.doi.org/10.1561/1500000042
Paradigm change in MIR
6

7. 1. Lawful - respecting all applicable laws and
regulations.
2. Ethical - respecting ethical principles and values.
3. Robust - both from a technical perspective while
taking into account its social environment. Avoid
intentional/unintentional harm.
7
Ethics guidelines for Trustworthy AI (2019)

8. KR1. Human
agency and
oversight
KR2. Technical
robustness and
safety
KR3. Privacy and
data governance
KR4.
Transparency
KR5. Diversity,
non-
discrimination
and fairness
KR6. Societal
and
environmental
well-being
KR7.
Accountability
7 Key Requirements for
Trustworthy AI
To be continuously evaluated
and addressed throughout the
AI system’s life cycle
8

9. Hupont, I., & Gomez, E. (2022). Documenting use cases in
the affective computing domain using Unified Modeling
Language. Affective Computing and Intelligence
Interaction https://arxiv.org/abs/2209.09666v1
9
From systems to use cases: example

10. Description
AI systems should empower human beings,
allowing them to make informed decisions
and fostering their fundamental rights.
At the same time, proper oversight
mechanisms need to be ensured, which can
be achieved through human-in-the-loop,
human-on-the-loop, and human-in-command
approaches.
Related topics
● Relevant user interfaces and HCIs.
● Ways to override or reverse the system output.
● Expertise needed to operate a system, how to
evaluate its correct operation.
Questions:
- How does an affective music recommender
may empower a listener?
- How listeners can take control of a music
recommender system?
KR1. Human agency and oversight

11. 11

12. Description
AI systems need to be resilient and secure.
They need to be safe, ensuring a fall back
plan in case something goes wrong, as well
as being accurate, reliable and reproducible.
That is the only way to ensure that also
unintentional harm can be minimized and
prevented.
Related topics
● Accuracy level, metrics,
● Robustness tests: unintended (e.g. noise)
or intended errors (e.g. attacks).
● Consideration of edge cases.
● Reproducibility, open science.
KR2. Technical robustness and safety
MIR considerations
● Different metrics for different tasks and
contexts.
● Robustness tests not widely applied.
● Reproducibility fostered in ISMIR
research.

13. Description
Besides ensuring full respect for privacy and
data protection, adequate data governance
mechanisms must also be ensured, taking
into account the quality and integrity of the
data, and ensuring legitimised access to data.
Related topics
● Protection of personal data (e.g. minimization).
● Privacy-preserving recommendations.
● Define how to collect, label, process, audit and monitor
the data that goes into developing the AI system.
● Datasets used for training and validation should be
relevant, without errors and representative: ensure data is
up to date, matches demographics, carry out sanity
checks.
KR3. Privacy and data governance

14. Challenges:
• Data sources, crossing,
reconciliation.
• Time dimension.
• User understandability and
control.
• Different legal approaches
(e.g. EU - GDPR vs US).
• Different levels of personal data:
○ Nominative data leading to the identification of
natural persons.
○ Data leading to identification through
“complex” processes.
Pierre Saurel, Francis Rousseaux, & Marc Danger. (2014). On The Changing Regulations of Privacy and Personal Information in MIR.
Proceedings of the 15th International Society for Music Information Retrieval Conference, 597–602.
https://doi.org/10.5281/zenodo.1416638
KR3. Privacy and data governance

15. J. S. Gómez-Cañón et al., "Music
Emotion Recognition: Toward new,
robust standards in personalized and
context-sensitive applications," in
IEEE Signal Processing Magazine, vol.
38, no. 6, pp. 106-114, Nov. 2021,
doi: 10.1109/MSP.2021.3106232.
KR3. Privacy and personalization

16. Description
The data, system and business models linked to AI
should be transparent.
Traceability mechanisms can help achieving this.
Moreover, AI systems and their decisions should be
explained in a manner adapted to the stakeholder
concerned.
Humans need to be aware that they are interacting with
an AI system, and must be informed of the system’s
capabilities and limitations.
Related topics
How the system is built and evaluated, training data, limitations.
How the system is monitored, e.g. logs.
How the system is controlled, e.g. how to interpret its outputs. Potential misuse.
Pre-determined changes, expected lifetime and
necessary maintenance/care measures.
Communication to users, e.g. generative models.
KR4. Transparency
https://algorithmic-transparency.ec.europa.eu/index_en

17. ● Transparency can serve to empower
people to challenge AI systems.
● Listeners → fake news,
impersonation.
● Artists → intellectual property, e.g.
training data has copyright,
infringement on the engineer.
Gómez, E., Blaauw, M., Bonada, J., Chandna, P., & Cuesta, H. (2018).
Deep learning for singing processing: Achievements, challenges and
impact on singers and listeners. arXiv preprint arXiv:1807.03046.
Sturm B., Iglesias M, Ben-Tal O, Miron M, Gómez E. Artificial
Intelligence and Music: Open Questions of Copyright Law and Engineering
Praxis. Arts. 2019; 8(3):115.https://doi.org/10.3390/arts8030115
KR4. Transparency: music generation

18. Description
Unfair bias must be avoided, as it could have multiple
negative implications, from the marginalization of
vulnerable groups, to the exacerbation of prejudice and
discrimination. Fostering diversity, AI systems should be
accessible to all, regardless of any disability, and involve
relevant stakeholders throughout their entire life cycle.
Related topics
Demographics, gender, sex, age, ethnicity, culture, religion,
sexual orientation, political orientation, culture….
Specific section later in the
second part of the tutorial!
KR5. Diversity, non-discrimination and fairness

19. Description
AI systems should benefit all human beings,
including future generations. It must hence be
ensured that they are sustainable and
environmentally friendly. Moreover, they
should take into account the environment,
including other living beings, and their social
and societal impact should be carefully
considered.
Related topics
● Impact on jobs and skills.
● Computing ressources.
Questions:
- How will MIR affect music workspaces?
e.g. instruments players, musicians, composers.
Tolan, S., Pesole, A., Martínez-Plumed, F., Fernández-Macías, E., Hernández-Orallo, J., & Gómez, E. (2021). Measuring the occupational impact of AI: tasks,
cognitive abilities and AI benchmarks. Journal of Artificial Intelligence Research, 71, 191-236.
Emilia Gómez (2020). Human and Machine Intelligence: A Music Information Retrieval Perspective. Keynote speech, 11th International Conference on
Computational Creativity.
KR6. Societal and environmental well-being

20. • Creative and performing artists
• Architects, planners, surveyors,
designers
• Artistic, cultural and culinary
associate professionals
• Creativity and resolution
• Accounting
• Business
• Communication
• Conceptualization
• Text comprehension
• Attention and search
• Quantitative reasoning
• AI exposure score
• Few benchmaking initiatives on
creative systems
Songul Tolan, Annarosa Pesole, Fernando Martínez-Plumed, Enrique
Fernandez-Macias, José Hernández-Orallo & Emilia Gómez,
“Measuring the Occupational Impact of AI: Tasks, Cognitive
Abilities and AI Benchmarks”, JRC Working Papers on Labour,
Education and Technology 2020-02, Joint Research Centre (Seville
site).
KR6. Societal well-being: jobs

21. Training
Evaluation: version identification,
accuracy-scalability plane.
Embedding distillation
techniques: less storage, faster
retrieval and similar accuracy
(Yesiler et al. 2022).
NIME Conference Environmental
Statement
https://eco.nime.org/
Traditional systems
DL systems
Scalability
Accuracy
F. Yesiler, J. Serrà, E. Gómez. Less is more: Faster and
better music version identification with embedding
distillation, ISMIR 2020.
Jukebox, Open AI (2020)
Evaluation
KR6. Environmental well-being

22. Description
Mechanisms should be put in place to ensure
responsibility and accountability for AI
systems and their outcomes. Auditability,
which enables the assessment of algorithms,
data and design processes plays a key role
therein, especially in critical applications.
Moreover, adequate an accessible redress
should be ensured.
Related topics
● Reproducibility and open data, code.
● Algorithmic audits. Questions:
- Which are the challenges to reproduce an
existing paper, e.g. audit an MIR system?
KR7. Accountability

23. Newly
addressed
Some
research
Strong
background
KR1. Human
agency and
oversight
KR2. Technical
robustness and
safety
KR3. Privacy and
data governance
KR4.
Transparency
KR5. Diversity,
non-discrimination
and fairness
KR6. Societal
and
environmental
well-being
KR7.
Accountability
7 Key Requirements for
Trustworthy AI
To be continuously evaluated
and addressed throughout the
AI system’s life cycle

24. From ethical guidelines to legal requirements
AI Act: https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:52021PC0206 **Under negotiation**
Legal requirements:
• Robustness, accuracy and cybersecurity.
• Human oversight (measures built into the
system and/or to be implemented by users).
• Ensure appropriate degree of transparency and
provide users with information on capabilities
and limitations of the system and how to use it.
• Technical documentation and logging
capabilities (traceability and auditability).
• High-quality and representative training,
validation and testing data.
• Risk management.
AI Act - Scope: AI systems (software products)
KR1. Human
agency and
oversight
KR2. Technical
robustness and
safety
KR3. Privacy and
data governance
KR4.
Transparency
KR5. Diversity,
non-discriminatio
n and fairness
KR6. Societal and
environmental
well-being
KR7.
Accountability
Unacceptable
risk
High risk
‘Transparency’ risk
Minimal or no risk
Prohibite
d
Permitted subject to
compliance with AI
requirements and ex-ante
conformity assessment
Permitted subject to
information/transparency
obligations
Permitted
with no
restrictions
*Not
mutually
exclusive

25. ● Risk management.
● Transparency of recommender systems, online
advertisement.
● External & independent auditing, internal
compliance function and public accountability.
● Data sharing with authorities and researchers.
● Crisis response cooperation
Digital Services Act:
https://ec.europa.eu/info/strategy/priorities-2019-2024/europe-fit-digital-age/digital-services-act-ensuring-s
afe-and-accountable-online-environment_en
Entering into force in Europe in 2023!
Digital Services Act - Scope: digital services (e.g. search engines, online platforms)
From ethical guidelines to legal requirements

26. PRINCIPLES
● Proportionality and Do No Harm
● Safety and security
● Fairness and non-discrimination
● Sustainability
● Right to Privacy, and Data Protection
● Human oversight and determination
● Transparency and explainability
● Responsibility and accountability
● Awareness and literacy
● Multi-stakeholder and adaptive governance and
collaboration
https://unesdoc.unesco.org/ark:/48223/pf0000381137
Towards worldwide recommendations

27. Part I
Trustworthy MIR:
Evaluation
27

28. 28
Excursus

29. In 1878 in Birka (Southeastern Sweden),
unburied Viking settlement from about 750 to 950
29
High-status,
Viking warrior,
male.
Weapons found in the grave.
(Image credit: Neil Price, Charlotte
Hedenstierna-Jonson, Torun Zachrisso, Anna
Kjellström; Copyright : Antiquity Publications Ltd.)
Illustration how the burial might have looked just
before it was closed in Viking times.
(Image credit: Drawing by Þórhallur Þráinsson;
Copyright Antiquity Publications Ltd.)

30. buried person
is female
2017, DNA test
30
XX
chromosomes
Second method (other
than looking on clothes,
arms, and setting)—the
DNA test—gave a better
insight.
Assumption that arms,
“non-female appearing” clothes,
etc., indicate a male warrior.

31. ?
This finding opened up new questions
31
All Viking warriors women?
Was she a warrior?
What was the social role of women in Viking time?

32. 32
There are blind spots when taking
only one perspective,
using a single method
with one metric.

33. 33
MIR evaluation:
from system-centered to
human-centered approaches

34. MIR evaluation - first steps: system-centered
34
2004 ISMIR 2004 Audio Description Contest
first international evaluation project in MIR
2005 first edition of the Music Information Retrieval Evaluation eXchange (MIREX),
organized by IMIRSEL (International Music IR Systems Evaluation Laboratory)
2011 MusiClef campaign
(later MediaEval series)
2012 Million Song Dataset Challenge
(Urbano et al., 2013)

35. MIR evaluation - mimicking users
35
● follow Cranfield paradigm for Information Retrieval (IR) evaluation
● trying to mimic the potential requests of the final users:
“removing actual users from the experiment but including a static user component:
the ground truth”
● 3 major simplifying assumptions:
○ 1. Relevance can be approximated by topical similarity.
○ 2. A single set of judgments for a topic is representative of the user population.
○ 3. Lists of relevant documents for each topic is complete (all relevant
documents are known).
(Urbano et al., 2013)
(Cleverdon, 1991)
(Vorhees, 2002)
mimicking users ≈ system-centered

36. MIR evaluation - considering the user: user-centered?
36
● the neglected user:
○ too simplistic view of “the user”
○ we need to model the user comprehensively
● still: no user involved?!
(Schedl et al., 2013)

37. 37
Nothing about us without us.

38. MIR evaluation - involving the user = (more) user-centered
38
● If you target user, you have to involve users.
● Typical questions to address:
○ (How) do users accomplish their tasks?
○ (How) do users accomplish the sub-tasks (if any) in their tasks?
○ How many steps do the users spend in finishing their tasks?
○ How long do the users spend in finishing their tasks? (task completion time)
● Important aspects→ Variations and differences across users
○ Individual differences and subjectivity
○ Still: Users groups will share similarities
(Liu & Hu, 2010)

39. MIR evaluation - from user-centered to human-centered evaluation
39
Users are not the only humans
involved in and impacted by
MIR systems!

40. Considering multiple stakeholders
40
music creators performers
music companies platform/service providers
end consumers/listeners
society
...

41. E.g., Stakeholders for (music) recommender systems
41
What is my target group?
Who else is affected/involved?
Also consider sub-groups!
(Jannach & Bauer, 2020, adapted)

42. What is the providers goal, need or intent?
Do these overlap with the users’ perspective?
Do they contradict?
Different stakeholders — task, intent, goal, need
42
What is the user’s task?
What is the user’s intent?
What does the user want?
What does the user need?
Are there multiple tasks, intents,
demands, needs?
Is this fair?
Is this desirable?
Who says that?

43. Different stakeholders — different risks
43
(Jannach & Jugovac, 2019)

44. 44
Striving toward trustworthy MIR,
we need to
assess our systems
concerning
the various perspectives.

45. Comprehensive evaluation is not one-size-fits all
45
● A one-size-fits-all evaluation strategy does not exist.
● Each scenario requires its fitting evaluation strategy.
● Different stakeholders may have different perspectives, goals, demands,
wishes, values, risks—potentially contradicting.

46. 46
Comprehensive
evaluation
Goal:
Getting an integrated big picture
of a system’s performance

47. Comprehensive evaluation = multi-* evaluation
47
multiple methods
multiple metrics
multiple datasets
multiple perspectives
…

48. Benefits
48
● Explore sophisticated issues more
holistically and widely
● Capture diverse, but complementary
phenomena
● Apply diverse methods to capture the
same phenomenon from possibly
different angles
● Neutralize biases inherent to evaluation
approaches
(Celik et al., 2018)

49. We need to thoughtfully configure the evaluation design space.
And we have to do this on several levels for a comprehensive evaluation.
49
(Zangerle & Bauer, 2022)
Framework for EValuating
Recommender systems (FEVR)

50. Typical differentiation of experiment types
50
Different (sub-)communities
→different terminology
● Computational or algorithmic
approaches
● User studies (in the lab or online)
● Field studies/experiment (using a
real-world system)
(Zangerle & Bauer, 2020)

51. The scenarios to evaluate are multifaceted
51
Potential overall goals:
● Convince/persuade user
● User satisfaction
● Replace user
● Assist user
● …
Target group:
● Individual users vs. user groups
● Novices vs experts
● Adults vs children
● Niche music vs mainstream listeners
● …
Situation of a user in session:
● Main task vs side task
● Self-initiated vs other-initiated
● Clear idea/goal vs exploration
● …
Starting point for evaluation setting:
● Ground truth exists vs subjective
perception (i.e., variation across users)
● Existing data vs data collection
○ Fit of data to goal
○ Representation of target group
● …

52. 52
What exactly do we want to find out?
What do we need to find out?
Is it relevant?
Does it matter in practice?
What is good enough?

53. Let us look at some variables of interest
53

54. Does my system accurately identify a song?
54
● accuracy-based metrics
(e.g., precision, recall, F-score)
● What is the harm of a false positive?
● What is the harm of a false negative?

55. For person who uploaded a song:
Does my system accurately identify a song?
55
● What is the harm of a false positive?
● What is the harm of a false negative?
TP marked as infringement, is infringement
FP marked as infringement, is not infringement
TN not marked as infringement, is not infringement
FN not marked as infringement, is infringement
Song identification to detect copyright infringement:

56. For copyright holder of song:
Does my system accurately identify a song?
56
● What is the harm of a false positive?
● What is the harm of a false negative?
TP marked as infringement, is infringement
FP marked as infringement, is not infringement
TN not marked as infringement, is not infringement
FN not marked as infringement, is infringement
Song identification to detect copyright infringement:

57. Is a MIR system fair?
57
Is my system fair for artists?
● Are artists fairly represented in a playlist?
● …in a dataset?
● …fair regarding gender of artists?
● …fair regarding genre?
● …fair regarding country of origin?
● …
Is my system fair for users?
● Are world-music fans as well served as
heavy-metal fans?
● Are gender minority users served well?
● Are novices served well?
● …
Do artists perceive it as fair?
Do users perceive it as fair?
Do artists/users perceive any differences?

58. 58
We need to ask a lot of question.
We need to ask the right questions.
We need to ask the right questions right.

59. Part II
Values in Practice:
Diversity and Fairness
59

60. Key Requirements for Trustworthy AI (AI-HLEG, 2018)
60
“In order to achieve Trustworthy AI, we
must enable inclusion and diversity
throughout the entire AI system’s life
cycle. Besides the consideration and
involvement of all affected
stakeholders throughout the process,
this also entails ensuring equal access
through inclusive design processes as
well as equal treatment. This
requirement is closely linked with the
principle of fairness.” (Section 1.5)

61. Recommendation on the Ethics of AI (UNESCO, 2020)
61
“AI actors should promote social justice and safeguard fairness and non-discrimination of any kind [...].
This implies an inclusive approach to ensuring that the benefits of AI technologies are available and
accessible to all, taking into consideration the specific needs of different age groups, cultural systems,
different language groups, persons with disabilities, girls and women, and disadvantaged, marginalized
and vulnerable people or people in vulnerable situations.” (Section III.2, Article 28)

62. (IS)MIR Initiatives
62
❖ Woman in Music Information Retrieval (WiMIR), 2011-present https://wimir.wordpress.com/
❖ Ethics guidelines for MIR, WiMIR workshop, Nov. 3 2019 (link)
❖ ISMIR Tutorials:
➢ Why is Greek Music Interesting? Towards an Ethics of MIR, Section 4, 2014 (link)
➢ Computational Approaches for Analysis of Non-Western Music Traditions, 2018 (link)
➢ Fairness, Accountability and Transparency in Music Information Research, 2019 (link)
❖ ISMIR Special Call for Papers: Cultural and Social Diversity in MIR (2021, 2022)
❖ TISMIR Call for Papers: Special Collection on Cultural Diversity in MIR (2022)
❖ NIME Principles & Code of Practice on Ethical Research https://www.nime.org/ethics/

63. Part II
Values in Practice:
Diversity
63

64. Organizing Team ISMIR 2022 (w/o volunteers)
64
https://ismir2022.ismir.net/team

65. Organizing Team ISMIR 2022 (w/o volunteers)
65
Is it a
diverse
team?
https://ismir2022.ismir.net/team

66. Organizing Team ISMIR 2022 (w/o volunteers)
66
Is it a
diverse
team?
Yes, but…
https://ismir2022.ismir.net/team

67. Organizing Team ISMIR 2022 (w/o volunteers)
67
Is it a
diverse
team?
Yes, but… No, but…
https://ismir2022.ismir.net/team

68. 68

69. 69
Measure
2.
Context &
Concept
1.
Impact
4.
Perception
3.
The Cycle of Diversity

70. Contexts & Concepts of Diversity (Steel et al., 2018)
70
An understanding of what
constitutes diversity,
abstracted from questions of
which attributes are relevant
to diversity in a specific context.
Background situation that
suggests attributes relevant
for assessing diversity.
Context Concept

71. Contexts & Concepts of Diversity (Steel et al., 2018)
71
An understanding of what
constitutes diversity,
abstracted from questions of
which attributes are relevant
to diversity in a specific context.
Background situation that
suggests attributes relevant
for assessing diversity.
Context Concept

72. Contexts & Concepts of Diversity (Steel et al., 2018)
72
An understanding of what
constitutes diversity,
abstracted from questions of
which attributes are relevant
to diversity in a specific context.
Background situation that
suggests attributes relevant
for assessing diversity.
Context Concept
Domain Agnostic
Domain Specific

73. Contexts & Concepts of Diversity (Steel et al., 2018)
73
An understanding of what
constitutes diversity,
abstracted from questions of
which attributes are relevant
to diversity in a specific context.
Background situation that
suggests attributes relevant
for assessing diversity.
Context Concept
Domain Agnostic
Domain Specific

74. Concepts of Diversity - Egalitarian (Steel et al., 2018)
74
Let L be the line-up of an electronic music festival.

75. Concepts of Diversity - Egalitarian (Steel et al., 2018)
75
Let L be the line-up of an electronic music festival.
If the artists part of L are distributed uniformly over
several styles of electronic music S = {s1 , ..., sn}, the
festival is diverse from an egalitarian perspective.

76. Concepts of Diversity - Normic (Steel et al., 2018)
76
Let L be the line-up of an electronic music festival,
S = {s1 , ..., sn } be styles of electronic music, and
Snd
the non-diverse norm be the Electronic Dance Music (EDM) style.

77. Concepts of Diversity - Normic (Steel et al., 2018)
77
Let L be the line-up of an electronic music festival,
S = {s1 , ..., sn } be styles of electronic music, and
Snd
the non-diverse norm be the Electronic Dance Music (EDM) style.
Then, we can assume that L is expected to be diverse in a normic
sense if the majority of artists performing do not play EDM i.e. ∉ Snd

78. 78
“When a measure becomes a target,
it ceases to be a good measure.”
Strathern (1997)

79. 79
Measures of Diversity
Index-based
Given a set 𝛀 of N elements, analyze their distribution into categories
and returns a single value:
- Compact representation
- Monodimensional

80. 80
Measures of Diversity
Index-based
Given a set 𝛀 of N elements, analyze richness and evenness returns a
single value:
- Compact representation
- Monodimensional
Distance-based
Given a set of 𝛀 of N elements, compute the pairwise distance between
elements & aggregate:
- Element-wise information
- Multidimensional

81. 81
Measures of Diversity - Index-based
Given a set 𝛀 of N elements,
Richness: number of categories present in the set 𝛀
Evenness: distribution of elements across the categories of 𝛀

82. 82
Measures of Diversity - Index-based
Given a set 𝛀 of N elements,
Richness: number of categories present in the set 𝛀
Evenness: distribution of elements across the categories of 𝛀
Valence
Arousal
Q1
Q2
Q3 Q4

83. 83
Measures of Diversity - Index-based
Richness: number of categories present in the set 𝛀 = 4 = N
Evenness: distribution of elements across the categories of 𝛀 = (1,1,1,1) = (n1, n2, n3, n4)
Valence
Arousal
Q1
Q2
Q3 Q4

84. 84
Measures of Diversity - Index-based
Richness: number of categories present in the set 𝛀 = 4 = N
Evenness: distribution of elements across the categories of 𝛀 = (1,1,1,1) = (n1, n2, n3, n4)
Valence
Arousal
Q1
Q2
Q3 Q4
Simpson’s
Diversity Index
(Simpson, 1949)
0 = no diversity
1 = high diversity

85. 85
Measures of Diversity - Index-based
Richness: number of categories present in the set 𝛀 = 4 = N
Evenness: distribution of elements across the categories of 𝛀 = (1,1,1,1) = (n1, n2, n3, n4)
Valence
Arousal
Q1
Q2
Q3 Q4
How diverse is the set of tracks
(in terms of emotion recognized)
according to the Simpson index?
Simpson’s
Diversity Index
(Simpson, 1949)

86. 86
Measures of Diversity - Index-based
Richness: number of categories present in the set 𝛀 = 4 = N
Evenness: distribution of elements across the categories of 𝛀 = (1,1,1,1) = (n1, n2, n3, n4)
Valence
Arousal
Q1
Q2
Q3 Q4
D(𝛀) = 1
N.B. quite egalitarian
Simpson’s
Diversity Index
(Simpson, 1949)

87. 87
Measures of Diversity - Index-based
Richness: number of categories present in the set 𝛀 = 4 = N
Evenness: distribution of elements across the categories of 𝛀 = (1,1,1,1) = (n1, n2, n3, n4)
Valence
Arousal
Q1
Q2
Q3 Q4 Valence
Arousal
Q1
Q2
Q3 Q4

88. 88
Measures of Diversity - Index-based
Richness: number of categories present in the set 𝛀 = 4 = N
Evenness: distribution of elements across the categories of 𝛀 = (1,1,1,1) = (n1, n2, n3, n4)
Valence
Arousal
Q1
Q2
Q3 Q4 Valence
Arousal
Q1
Q2
Q3 Q4
D(𝛀) = 1
???

89. 89
Measures of Diversity - Distance-based
Given a set of 𝛀 of N elements,
compute the elements pairwise
distance (Euclidean, Jaccard,
Cosine, etc.) , and aggregate.

90. 90
Measures of Diversity - Distance-based (Porcaro and Gómez, 2019)
2-dimensional projection
of tag-embedding space

91. 91
Measures of Diversity - Distance-based (Porcaro and Gómez, 2019)
2-dimensional projection
of tag-embedding space
Computed pairwise
distance within clusters

92. 92
Measures of Diversity - Distance-based (Porcaro and Gómez, 2019)
2-dimensional projection
of tag-embedding space
Computed pairwise
distance within clusters
The one with maximum
average distance is the
most diverse!

93. 93
Measures of Diversity - Distance-based (Porcaro and Gómez, 2019)
2-dimensional projection
of tag-embedding space
Computed pairwise
distance within clusters
The one with maximum
distance is the most
diverse!

94. 94
Measures of Diversity - Distance-based (Porcaro and Gómez, 2019)

95. 95
Measures of Diversity - Distance-based (Porcaro and Gómez, 2019)
Jazz vocal - Swing
???
Ballad - Love

96. 96
Do differences in system-measures directly translate to
the same differences in user-measures? How do those
differences affect end users?
Construct Validity (Urbano et al., 2013)

97. 97
Perceptions of Diversity (Porcaro et al., 2022)
Pretty Lights
(US)
Downtempo
Technoboy
(IT)
Hardstyle
Bonobo
(UK)
Downbeat
Laurent Garnier
(FR)
Techno
The Blessed Madonna
(US)
House
Helena Hauff
(DE)
Techno
Miss DJAX
(NL)
Hardcore
Beatrice Dillon
(UK)
Experimental

98. 98
Perceptions of Diversity (Porcaro et al., 2022)
Pretty Lights
(US)
Downtempo
Technoboy
(IT)
Hardstyle
Bonobo
(UK)
Downbeat
Laurent Garnier
(FR)
Techno
The Blessed Madonna
(US)
House
Helena Hauff
(DE)
Techno
Miss DJAX
(NL)
Hardcore
Beatrice Dillon
(UK)
Experimental
Which one is the
most diverse
playlist?

99. 99
Perceptions of Diversity (Porcaro et al., 2022)
Pretty Lights
(US)
Downtempo
Technoboy
(IT)
Hardstyle
Bonobo
(UK)
Downbeat
Laurent Garnier
(FR)
Techno
The Blessed Madonna
(US)
House
Helena Hauff
(DE)
Techno
Miss DJAX
(NL)
Hardcore
Beatrice Dillon
(UK)
Experimental
Both playlists have:
- Same number of styles
- Only Western-based DJs
- Only white skin DJs
- Only one sex (biological
aspect)

100. 100
Perceptions of Diversity (Porcaro et al., 2022)
Pretty Lights
(US)
Downtempo
Technoboy
(IT)
Hardstyle
Bonobo
(UK)
Downbeat
Laurent Garnier
(FR)
Techno
The Blessed Madonna
(US)
House
Helena Hauff
(DE)
Techno
Miss DJAX
(NL)
Hardcore
Beatrice Dillon
(UK)
Experimental
According to
computational
diversity metrics,
NO statistical difference.

101. 101
Perceptions of Diversity (Porcaro et al., 2022)
Pretty Lights
(US)
Downtempo
Technoboy
(IT)
Hardstyle
Bonobo
(UK)
Downbeat
Laurent Garnier
(FR)
Techno
The Blessed Madonna
(US)
House
Helena Hauff
(DE)
Techno
Miss DJAX
(NL)
Hardcore
Beatrice Dillon
(UK)
Experimental
but…

102. 102
Perceptions of Diversity (Porcaro et al., 2022)
Pretty Lights
(US)
Downtempo
Technoboy
(IT)
Hardstyle
Bonobo
(UK)
Downbeat
Laurent Garnier
(FR)
Techno
The Blessed Madonna
(US)
House
Helena Hauff
(DE)
Techno
Miss DJAX
(NL)
Hardcore
Beatrice Dillon
(UK)
Experimental
Asking to 110
participants,
72% of them agree that
List Blue is more diverse
than List Green

103. 103
Perceptions of Diversity (Porcaro et al., 2022)
Pretty Lights
(US)
Downtempo
Technoboy
(IT)
Hardstyle
Bonobo
(UK)
Downbeat
Laurent Garnier
(FR)
Techno
The Blessed Madonna
(US)
House
Helena Hauff
(DE)
Techno
Miss DJAX
(NL)
Hardcore
Beatrice Dillon
(UK)
Experimental
Most of the participants
employed a normic
concept of diversity…

104. 104
Perceptions of Diversity (Porcaro et al., 2022)
Pretty Lights
(US)
Downtempo
Technoboy
(IT)
Hardstyle
Bonobo
(UK)
Downbeat
Laurent Garnier
(FR)
Techno
The Blessed Madonna
(US)
House
Helena Hauff
(DE)
Techno
Miss DJAX
(NL)
Hardcore
Beatrice Dillon
(UK)
Experimental
Most of the participants
employed a normic
concept of diversity…
… while our model works
under an egalitarian
concept.

105. 105
N.B.

106. 106
MIR and Subjectivity
MIR systems include a great level of subjectivity:
- Track A and Track B are very similar
- Track A is happier than Track B
- Playlist A is less diverse than Playlist B
- This track is 80% EDM and 20% Tech House

107. 107
MIR and Subjectivity
MIR systems include a great level of subjectivity:
- Track A and Track B are very similar
- Track A is happier than Track B
- Playlist A is less diverse than Playlist B
- This track is 80% EDM and 20% Tech House
Human annotations are fundamental resources,
but here comes the problem…

108. 108
Agreement and Perceptions
- Listeners trained in classical music tend to disagree more on perceived
emotions, than those not trained [Schedl et al., 2018]
- Preference, familiarity, and lyrics comprehension increase agreement for
emotions corresponding to quadrants Q1 and Q3. [Gómez-Cañón et al., 2020]
- Current emotional state of users influences their perception of music similarity
[Flexer et al., 2021]

109. 109
Diversity is a means to an end, not an end in itself.

110. Higher education → prepare students for life (allow them to interact with
students different socio-cultural backgrounds).
Media studies → reaching democratic goals (e.g., informed citizenry, inclusive
public discourse).
Economics → foster innovation; hedging against ignorance; mitigating lock-in;
accommodating plural perspectives.
110
Impacts of Diversity

111. Higher education → prepare students for life (allow them to interact with
students different socio-cultural backgrounds).
Media studies → reaching democratic goals (e.g., informed citizenry, inclusive
public discourse).
Economics → foster innovation; hedging against ignorance; mitigating lock-in;
accommodating plural perspectives.
111
Impacts of Diversity

112. Higher education → prepare students for life (allow them to interact with
students different socio-cultural backgrounds).
Media studies → reaching democratic goals (e.g., informed citizenry, inclusive
public discourse).
Economics → foster innovation; hedging against ignorance; mitigating lock-in;
accommodating plural perspectives.
112
Impacts of Diversity

113. In the MIR field, which are the ends wherein the
fostering of diversity may make a difference, that
as----------------community we should care about?
113

114. Part II
Values in Practice:
Fairness
114

115. ● General notions of Fairness and Bias
● Example of fairness studies in MIR and
recommendations:
○ What is fair in Music recommendation?
○ Gender imbalance in music Recsys
○ Fairness for users
○ Music record labels
○ Cover song identification
Overview

116. ● Fairness: Identification and prevention of situations where individuals or a
group are systematically disfavored, e.g., based on their characteristics
● Fairness is an interdisciplinary topic
● It is based on normative ideas of what it means to treat people “fairly”
Introduction - Fairness

117. May refer to:
● societal biases
● statistical biases
...
Bias as an objective deviation/imbalance in a model,
measure or data compared to an intended target
Introduction - Bias

118. 118
Bias: we refer to a fact of the system without making
any inherently normative judgment
Fairness: we use it to discuss the normative aspects
of the system and its effects

119. Fairness vs Bias
BIAS FAIRNESS

120. ● No single general answer to fairness, ethics, etc.
● Fairness hardly will be defined only with clear, universal objectives
but:
● We can identify/measure/mitigate specific harms
● Then, look for general strategies or principles
What is fair? Fair for who?

121. The ML community has begun to address claims of algorithmic
bias under the rubric of fairness, accountability, and transparency.
We should clearly define:
● Assumptions
● Goals or principles that we are based on
● Methods used to evaluate the ability to meet those goals
● How to assess the relevance and appropriateness of those
goals to the social problem in question
What is fair? Fair for who?
[*] Redistribution and Rekognition: A Feminist Critique of Algorithmic Fairness [West, 2020]

122. The ML community has begun to address claims of algorithmic
bias under the rubric of fairness, accountability, and transparency.
We should clearly define:
● Assumptions
● Goals or principles that we are based on
● Methods used to evaluate the ability to meet those goals
● How to assess the relevance and appropriateness of those
goals to the social problem in question
What is fair? Fair for who?
[*] Redistribution and Rekognition: A Feminist Critique of Algorithmic Fairness [West, 2020]

123. ● Distributional harm:
Harmful distribution of
resources or outcomes
● Representational harm:
Harmful internal or external
representation
General Concepts - I
Popularity
Recommended
Fairness in Information Access Systems [Ekstrand et al., 2022]

124. ● Distributional harm:
Harmful distribution of
resources or outcomes
● Representational harm:
Harmful internal or external
representation
General Concepts - I
Jamaica
reggae
Mississippi
blues

125. ● Individual fairness: Similar individuals have similar experience/treatment
● Group fairness: Different groups have similar experience/treatment
○ Sensitive attribute: Attribute identifying group membership
○ Disparate treatment: Groups intentionally treated differently
○ Disparate impact: Groups receive outcomes at different rates
○ Disparate mistreatment: Groups receive erroneous effects at different rates
General Concepts - II

126. ● Individual fairness: Similar individuals have similar experience/treatment
● Group fairness: Different groups have similar experience/treatment
○ Sensitive attribute: Attribute identifying group membership
○ Disparate treatment: Groups intentionally treated differently
○ Disparate impact: Groups receive outcomes at different rates
○ Disparate mistreatment: Groups receive erroneous effects at different rates
General Concepts - II

127. ● Individual fairness: Similar individuals have similar experience/treatment
● Group fairness: Different groups have similar experience/treatment
○ Sensitive attribute: Attribute identifying group membership
○ Disparate treatment: Groups intentionally treated differently
○ Disparate impact: Groups receive outcomes at different rates
○ Disparate mistreatment: Groups receive erroneous effects at different rates
General Concepts - II

128. ● Individual fairness: Similar individuals have similar experience/treatment
● Group fairness: Different groups have similar experience/treatment
○ Sensitive attribute: Attribute identifying group membership
○ Disparate treatment: Groups intentionally treated differently
○ Disparate impact: Groups receive outcomes at different rates
○ Disparate mistreatment: Groups receive erroneous effects at different rates
General Concepts - II

129. ● Individual fairness: Similar individuals have similar experience/treatment
● Group fairness: Different groups have similar experience/treatment
○ Sensitive attribute: Attribute identifying group membership
○ Disparate treatment: Groups intentionally treated differently
○ Disparate impact: Groups receive outcomes at different rates
○ Disparate mistreatment: Groups receive erroneous effects at different rates
General Concepts - II

130. ● We want to make sure that different applications are free of undesired biases
and prevent any kind of discrimination
● Usually RS are user-centered: Maximize user satisfaction is the main goal
● Consider systems’ impact on multiple groups of individuals
● How platforms can be fair for the artists?
Provider vs Consumer Fairness

131. ● We want to make sure that different applications are free of undesired biases
and prevent any kind of discrimination
● Usually RS are user-centered: Maximize user satisfaction is the main goal
● Consider systems’ impact on multiple groups of individuals
● How platforms can be fair for the artists?
Provider vs Consumer Fairness

132. ● We want to make sure that different applications are free of undesired biases
and prevent any kind of discrimination
● Usually RS are user-centered: Maximize user satisfaction is the main goal
● Consider systems’ impact on multiple groups of individuals
● How platforms can be fair for the artists?
Provider vs Consumer Fairness

133. ● We want to make sure that different applications are free of undesired biases
and prevent any kind of discrimination
● Usually RS are user-centered: Maximize user satisfaction is the main goal
● Consider systems’ impact on multiple groups of individuals
● How platforms can be fair for the artists?
Provider vs Consumer Fairness

134. ● The leading questions are:
○ How do artists feel affected by current systems?
○ What do artists want to see improved in future?
○ What concrete ideas or even algorithmic decisions could be adopted in
future systems?
What is fair? exploring the artists' perspective on the fairness of music streaming platforms [Ferraro et al., 2021]
What is fair in Music recommendation?

135. 135
Design of
semi-structured
interviews
Documents
preparation
Selected participants
based on diversity
Conducting
interviews
Interview
transcription
Coding
Analysis of
Results
Methodology - Semi-structured interviews and Qualitative Content Analysis
Qualitative content analysis [Mayring, 2004]

136. 136
Topics

137. - What do artists think about the platforms' opportunities and power to influence
the users' listening behavior?
- We see a strong tendency against influencing users
Results - Interviews

138. 138
"I don’t see why we should tell the users which
genres they should listen to"

139. - All artists agreed that the platforms should promote content by female artists
to reach a gender balance in what users consume
Results - Interviews

140. 140
"I think there should be actions to correct some
biases. The question is in which cases it should be
corrected and in which not. In heavy metal music, I
imagine that there aren’t many female singers. Maybe
we could give them more visibility, otherwise they would
never be seen"

141. 141
"[...] the population of the world is 50% women. So it
would be ridiculous if the system wouldn’t recommend
them.”

142. Based the interviews we focus on the topic of gender balance
● Qualitative approach:
○ Artists express that platforms should promote gender balance
● Quantitative approach:
○ Using 2 datasets of user listening data, evaluate how different users’ history is compared to the
(CF) recommendations
○ What is the effect in user consumption in the long term?
142
Break the Loop: Gender Imbalance in Music Recommenders [Ferraro, et al., 2021]
Gender Imbalance in Music Recsys

143. - Identify: We identify that these artists expect a balanced representation of
artists according to their gender
- Measure: Representation, Exposure and Utility
- Mitigate: Simulation approach to compare longitudinal effects considering
feedback loops.
Gender Imbalance in Music Recsys

144. - 3 sources of unfairness:
- Representation: Similar proportion to users history
- Exposure: Users have to put more effort to reach the first female artist in the recommendation
- Utiliy: Model make more “mistakes” for female artists than for male artists
Similar results in:
Exploring Artist Gender Bias in Music Recommendation [Shakespeare et al., 2020]
Gender Imbalance in Music Recsys - Results

145. Trained
model
Dataset with user
interactions
Simulate user interactions

146. Trained
model
...
Generate recommendations for users
(User X)
Simulate user interactions

147. Trained
model
Simulate user interactions with the
recommendations
...
(User X)
Simulate user interactions

148. Trained
model
Retrain the model
(User X)
Simulate user interactions

149. Trained
model
… repeat the process and
evaluate recommendations
...
(User X)
Simulate user interactions

150. 150
Feedback Loop

151. 151
Feedback Loop

152. - Exploring Longitudinal Effects of Session-based Recommendations. In Proceedings of
the 14th ACM Conference on Recommender Systems [Ferraro, Jannach & Serra, 2020]
- Consumption and performance: understanding longitudinal dynamics of recommender
systems via an agent-based simulation framework [Zhang et al., 2019]
- Longitudinal impact of preference biases on recommender systems’ performance [Zhou,
Zhang, & Adomavicius, 2021]
- The Dual Echo Chamber: Modeling Social Media Polarization for Interventional
Recommending [Donkers & Ziegler, 2021]
152
References - Simulations

153. - Exploring User Opinions of Fairness in Recommender Systems [Smith et al.,
2020]
- Fairness and Transparency in Recommendation: The Users’ Perspective
[Sonboli et al., 2021]
- The multisided complexity of fairness in recommender systems [Sonboli et al.,
2022]
References - Fairness for Users

154. - The Unfairness of Popularity Bias in Music Recommendation: A Reproducibility
Study [Kowald, Schedl & Lex, 2020]
- Support the underground: characteristics of beyond-mainstream music
listeners [Kowald et al., 2021]
- Analyzing Item Popularity Bias of Music Recommender Systems: Are Different
Genders Equally Affected? [Lesota et al., 2021]
References - Unfairness for users

155. Bias and Feedback Loops in Music Recommendation: Studies on Record Label
Impact [Knees, Ferraro & Hübler, 2022]
Music Record Labels

156. Music Record Labels
Bias and Feedback Loops in Music Recommendation: Studies on Record Label
Impact [Knees, Ferraro & Hübler, 2022]

157. Music Record Labels
Bias and Feedback Loops in Music Recommendation: Studies on Record Label
Impact [Knees, Ferraro & Hübler, 2022]

158. Assessing Algorithmic Biases for Musical Version Identification [Yesiler et al., 2022]
- Multiple stakeholders: Composers and other artists
- Compare 5 systems
- Use different attributes of the songs/artists
They Identify algorithmic biases in cover song id. systems that may lead to differences in
performance (dis)favor different groups of artists, e.g.:
- Tracks after 2000 work better
- Larger gap between release year lead to worse performance
- Learning-based systems perform better for underrepresented artists
Cover Song Identification

159. Assessing Algorithmic Biases for Musical Version Identification [Yesiler et al., 2022]
- Multiple stakeholders: Composers and other artists
- Compare 5 systems
- Use different attributes of the songs/artists
They Identify algorithmic biases in cover song id. systems that may lead to differences in
performance (dis)favor different groups of artists, e.g.:
- Tracks after 2000 work better
- Larger gap between release year lead to worse performance
- Learning-based systems perform better for underrepresented artists
Cover Song Identification

160. References
160

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