1
00:00:00,050 --> 00:00:03,110
Lesson methods and tools for conducting AI audits.

2
00:00:03,140 --> 00:00:09,290
Conducting AI audits is a critical component of AI governance, ensuring that AI systems are transparent,

3
00:00:09,290 --> 00:00:12,260
ethical, and compliant with regulatory standards.

4
00:00:12,740 --> 00:00:18,290
The methods and tools used for AI audits encompass a range of technical, procedural, and analytical

5
00:00:18,320 --> 00:00:23,330
techniques designed to assess the performance, fairness, and accountability of AI systems.

6
00:00:23,900 --> 00:00:30,770
AI audits aim to identify and mitigate risks associated with AI deployments, including biases, privacy

7
00:00:30,770 --> 00:00:33,380
issues, and operational inefficiencies.

8
00:00:34,190 --> 00:00:39,560
The first step in conducting an AI audit involves defining the scope and objectives.

9
00:00:39,590 --> 00:00:45,530
This stage requires clear articulation of the audit's goals, such as evaluating compliance with data

10
00:00:45,530 --> 00:00:51,500
protection regulations, assessing the accuracy and fairness of predictive models, or verifying the

11
00:00:51,500 --> 00:00:55,610
robustness of AI algorithms against adversarial attacks.

12
00:00:56,450 --> 00:01:01,930
Setting precise objectives helps in selecting the appropriate methods and tools for the audit, thereby

13
00:01:01,930 --> 00:01:05,260
ensuring that the audit is comprehensive and effective.

14
00:01:06,250 --> 00:01:12,040
One of the primary methods used in AI audits is model validation, which involves verifying that the

15
00:01:12,040 --> 00:01:14,350
AI model performs as intended.

16
00:01:14,740 --> 00:01:20,860
This process includes testing the model on various data sets to ensure consistency in its predictions.

17
00:01:21,250 --> 00:01:27,550
Cross validation techniques, such as k fold validation, are commonly used to assess the model's generalizability

18
00:01:27,550 --> 00:01:30,250
by dividing the data set into multiple subsets.

19
00:01:30,280 --> 00:01:35,380
Training the model on each subset and evaluating its performance on the remaining data.

20
00:01:35,890 --> 00:01:41,770
Additionally, sensitivity analysis is conducted to understand how changes in input variables affect

21
00:01:41,770 --> 00:01:47,530
the model's output, which helps in identifying any potential biases or vulnerabilities in the model.

22
00:01:49,240 --> 00:01:52,870
Fairness assessment is another crucial aspect of AI audits.

23
00:01:52,960 --> 00:01:59,300
This involves evaluating the AI system for potential biases that may lead to discriminatory Outcomes.

24
00:01:59,630 --> 00:02:05,600
Techniques such as disparate impact analysis and fairness aware machine learning are employed to measure

25
00:02:05,600 --> 00:02:06,980
and mitigate biases.

26
00:02:07,850 --> 00:02:13,640
Disparate impact analysis compares the outcomes of the AI system across different demographic groups

27
00:02:13,640 --> 00:02:17,540
to ensure that no group is disproportionately disadvantaged.

28
00:02:17,570 --> 00:02:23,660
Fairness aware machine learning algorithms are designed to minimize bias during the model training process,

29
00:02:23,660 --> 00:02:26,840
ensuring equitable treatment of all individuals.

30
00:02:28,400 --> 00:02:34,610
Transparency and explainability are essential components of AI audits as they enable stakeholders to

31
00:02:34,640 --> 00:02:37,940
understand the decision making process of AI systems.

32
00:02:38,450 --> 00:02:44,540
Tools such as Shap and Lime are widely used to provide interpretable insights into complex models.

33
00:02:45,080 --> 00:02:51,080
Shap values offer a unified framework to explain the output of any machine learning model, by attributing

34
00:02:51,080 --> 00:02:54,110
the contribution of each feature to the final prediction.

35
00:02:54,680 --> 00:03:01,250
Lime, on the other hand, approximates the model locally around a prediction to generate human understandable

36
00:03:01,250 --> 00:03:02,420
explanations.

37
00:03:02,690 --> 00:03:09,050
These tools are invaluable for auditors in assessing the transparency of AI systems and ensuring that

38
00:03:09,050 --> 00:03:12,560
the decision making process is comprehensible to end users.

39
00:03:13,460 --> 00:03:17,600
Data privacy and security are critical concerns in AI audits.

40
00:03:17,630 --> 00:03:23,390
Auditors must ensure that AI systems comply with data protection regulations, such as the General Data

41
00:03:23,390 --> 00:03:27,140
Protection Regulation and the California Consumer Privacy Act.

42
00:03:27,170 --> 00:03:32,780
Techniques such as differential privacy and federated learning are employed to protect sensitive data,

43
00:03:32,780 --> 00:03:36,110
while enabling AI model training and evaluation.

44
00:03:37,370 --> 00:03:43,070
Differential privacy introduces noise into the data to prevent the identification of individual records,

45
00:03:43,070 --> 00:03:44,870
thereby preserving privacy.

46
00:03:45,290 --> 00:03:50,600
Federated learning, on the other hand, allows AI models to be trained across multiple decentralized

47
00:03:50,600 --> 00:03:53,510
devices or servers without sharing raw data.

48
00:03:53,540 --> 00:04:01,490
Enhancing data security and privacy Operational risk assessment is another vital component of AI audits,

49
00:04:01,490 --> 00:04:05,570
focusing on the reliability and robustness of AI systems.

50
00:04:06,050 --> 00:04:12,080
Stress testing and scenario analysis are used to evaluate how AI systems perform under various conditions,

51
00:04:12,080 --> 00:04:15,380
including edge cases and adversarial scenarios.

52
00:04:15,770 --> 00:04:21,230
Stress testing involves subjecting the AI system to extreme conditions to assess its resilience and

53
00:04:21,230 --> 00:04:23,330
identify potential failure points.

54
00:04:23,750 --> 00:04:30,140
Scenario analysis, meanwhile, explores the impact of different hypothetical situations on the AI system's

55
00:04:30,140 --> 00:04:36,170
performance, helping auditors understand the potential risks and devise mitigation strategies.

56
00:04:37,640 --> 00:04:44,180
Ethical considerations are integral to AI audits, requiring auditors to evaluate the ethical implications

57
00:04:44,180 --> 00:04:45,530
of AI deployments.

58
00:04:45,560 --> 00:04:51,920
This involves assessing the AI systems alignment with ethical principles such as beneficence, non-maleficence,

59
00:04:51,950 --> 00:04:54,050
autonomy, and justice.

60
00:04:54,590 --> 00:05:00,550
Auditors must ensure that AI systems are designed and deployed in a manner that promotes societal well-being,

61
00:05:00,550 --> 00:05:06,190
minimizes harm, respects individual autonomy, and ensures fair and equitable treatment.

62
00:05:06,190 --> 00:05:11,890
Ethical impact assessments and stakeholder engagement are essential tools in this process, enabling

63
00:05:11,890 --> 00:05:17,800
auditors to gather diverse perspectives and evaluate the broader societal impacts of AI systems.

64
00:05:19,510 --> 00:05:26,050
The use of audit frameworks and standards is crucial for ensuring consistency and rigor in AI audits.

65
00:05:26,500 --> 00:05:32,530
Frameworks such as the AI audit Framework by the UK Information Commissioner's Office and the NIST AI

66
00:05:32,560 --> 00:05:37,210
Risk Management Framework provides structured approaches for conducting AI audits.

67
00:05:37,690 --> 00:05:43,840
These frameworks outline key principles, criteria, and processes for evaluating AI systems, helping

68
00:05:43,840 --> 00:05:49,000
auditors systematically assess compliance performance and ethical considerations.

69
00:05:49,000 --> 00:05:54,430
Adherence to such frameworks ensures that AI audits are conducted in a standardized and transparent

70
00:05:54,430 --> 00:05:57,600
manner, facilitating accountability and trust.

71
00:05:58,950 --> 00:06:04,740
In practice, AI audits often involve a combination of automated tools and human expertise.

72
00:06:05,040 --> 00:06:10,440
Automated tools such as algorithmic auditing software and machine learning platforms streamline the

73
00:06:10,440 --> 00:06:16,620
technical aspects of the audit, including data analysis, model validation, and bias detection.

74
00:06:16,980 --> 00:06:22,680
However, human expertise is indispensable in interpreting the results, making ethical judgments,

75
00:06:22,680 --> 00:06:24,540
and engaging with stakeholders.

76
00:06:24,570 --> 00:06:30,750
The collaboration between automated tools and human auditors ensures a comprehensive and nuanced evaluation

77
00:06:30,750 --> 00:06:32,010
of AI systems.

78
00:06:33,120 --> 00:06:38,130
The effectiveness of AI audits depends on continuous monitoring and iterative improvement.

79
00:06:38,730 --> 00:06:45,930
AI systems are dynamic and evolve over time, necessitating ongoing audits to ensure sustained compliance

80
00:06:45,930 --> 00:06:47,100
and performance.

81
00:06:47,430 --> 00:06:52,560
Continuous monitoring involves tracking key performance indicators and conducting periodic audits to

82
00:06:52,590 --> 00:06:54,540
identify any emerging issues.

83
00:06:54,580 --> 00:07:00,700
Iterative improvement, meanwhile, involves refining the AI model and its underlying processes based

84
00:07:00,700 --> 00:07:06,850
on audit findings, ensuring that the AI system remains aligned with ethical, legal and operational

85
00:07:06,850 --> 00:07:07,690
standards.

86
00:07:09,100 --> 00:07:14,740
In conclusion, conducting AI audits is a multifaceted process that involves a range of methods and

87
00:07:14,740 --> 00:07:21,280
tools to evaluate the performance, fairness, transparency, privacy, security, and ethical implications

88
00:07:21,280 --> 00:07:22,360
of AI systems.

89
00:07:22,900 --> 00:07:23,950
Model validation.

90
00:07:23,950 --> 00:07:25,000
Fairness assessment.

91
00:07:25,030 --> 00:07:31,240
Transparency tools, privacy techniques, operational risk assessment, ethical evaluations and audit

92
00:07:31,240 --> 00:07:33,940
frameworks are essential components of this process.

93
00:07:34,540 --> 00:07:39,640
The integration of automated tools and human expertise, along with continuous monitoring and iterative

94
00:07:39,640 --> 00:07:44,890
improvement, ensures that AI audits are comprehensive, rigorous, and effective.

95
00:07:45,250 --> 00:07:51,580
By employing these methods and tools, auditors can ensure that AI systems are trustworthy, accountable,

96
00:07:51,580 --> 00:07:53,860
and aligned with societal values.