1
00:00:00,050 --> 00:00:01,250
Lesson model selection.

2
00:00:01,280 --> 00:00:03,110
Accuracy versus interpretability.

3
00:00:03,140 --> 00:00:08,480
Model selection is a pivotal step in the AI development life cycle, especially during the planning

4
00:00:08,480 --> 00:00:09,260
phase.

5
00:00:09,710 --> 00:00:15,590
The balance between accuracy and interpretability is often a crucial decision point for AI developers,

6
00:00:15,590 --> 00:00:18,890
data scientists, and AI governance professionals.

7
00:00:19,370 --> 00:00:21,140
Understanding these two dimensions.

8
00:00:21,170 --> 00:00:27,500
Accuracy and interpretability can significantly influence the efficacy and adoption of AI systems.

9
00:00:28,550 --> 00:00:34,460
Accuracy refers to the ability of a model to correctly predict outcomes or classify data points.

10
00:00:34,490 --> 00:00:41,630
It is often quantified using metrics such as precision recall, F1 score, and overall accuracy rate.

11
00:00:42,380 --> 00:00:47,660
High accuracy is typically desirable because it implies that the model is performing its intended task

12
00:00:47,660 --> 00:00:48,470
effectively.

13
00:00:48,800 --> 00:00:54,470
For instance, in a medical diagnosis scenario, a highly accurate model would correctly identify the

14
00:00:54,470 --> 00:00:58,610
presence or absence of a disease, thereby improving patient outcomes.

15
00:00:58,640 --> 00:01:03,200
However, focusing solely on accuracy can sometimes be misleading.

16
00:01:03,320 --> 00:01:08,360
High accuracy might be achieved at the expense of other important factors such as generalizability,

17
00:01:08,360 --> 00:01:09,980
robustness, and fairness.

18
00:01:11,570 --> 00:01:16,760
Interpretability, on the other hand, involves the extent to which a human can understand the decisions

19
00:01:16,760 --> 00:01:18,620
or predictions made by a model.

20
00:01:19,010 --> 00:01:24,770
In simpler terms, it is about how transparent the model is in explaining its inner workings and outputs.

21
00:01:25,310 --> 00:01:31,760
Interpretability is essential for several reasons, including regulatory compliance, ethical considerations,

22
00:01:31,760 --> 00:01:33,140
and user trust.

23
00:01:33,590 --> 00:01:40,100
For example, in financial services, regulatory frameworks often require explanations for credit approval

24
00:01:40,100 --> 00:01:43,250
decisions to ensure fairness and transparency.

25
00:01:43,880 --> 00:01:48,980
A highly interpretable model can help meet these requirements by providing clear and understandable

26
00:01:48,980 --> 00:01:50,630
reasons for its decisions.

27
00:01:52,550 --> 00:01:57,050
Balancing accuracy and interpretability often requires trade offs.

28
00:01:57,320 --> 00:02:04,700
Complex models like deep neural networks and ensemble methods tend to have high accuracy but low interpretability.

29
00:02:05,210 --> 00:02:10,490
These models can capture intricate patterns in data, leading to superior performance in tasks such

30
00:02:10,490 --> 00:02:15,020
as image recognition, natural language processing, and predictive analytics.

31
00:02:15,260 --> 00:02:20,450
However, their complexity makes it difficult to understand how individual predictions are made.

32
00:02:20,480 --> 00:02:26,960
For instance, a deep neural network with multiple hidden layers might achieve high accuracy in classifying

33
00:02:26,990 --> 00:02:33,200
images, but explaining why a particular image was classified in a specific way can be challenging.

34
00:02:34,580 --> 00:02:40,700
Conversely, simpler models like linear regression, decision trees, and logistic regression are generally

35
00:02:40,700 --> 00:02:44,480
more interpretable, but might sacrifice some degree of accuracy.

36
00:02:45,290 --> 00:02:50,360
These models provide clear insights into how input features influence predictions.

37
00:02:50,390 --> 00:02:56,330
For example, a linear regression model can easily show the relationship between predictor variables

38
00:02:56,330 --> 00:03:02,400
and the outcome variable, making it straightforward for stakeholders to understand the model's behavior.

39
00:03:02,970 --> 00:03:08,940
However, such simplicity might limit the model's ability to capture complex relationships in the data,

40
00:03:08,940 --> 00:03:12,900
potentially leading to lower accuracy in certain applications.

41
00:03:14,520 --> 00:03:20,010
The decision on whether to prioritize accuracy or interpretability is context dependent.

42
00:03:20,190 --> 00:03:25,770
In high stakes domains such as healthcare, finance, and criminal justice, interpretability often

43
00:03:25,770 --> 00:03:29,880
takes precedence due to the need for accountability and transparency.

44
00:03:30,540 --> 00:03:36,300
For example, in health care, clinicians must understand the rationale behind a model's diagnostic

45
00:03:36,300 --> 00:03:39,480
recommendation to trust and adopt the technology.

46
00:03:39,480 --> 00:03:45,450
An interpretable model can provide valuable insights into the factors influencing a diagnosis, thereby

47
00:03:45,480 --> 00:03:49,320
facilitating informed decision making and enhancing patient trust.

48
00:03:49,920 --> 00:03:55,860
On the other hand, in domains where the primary objective is to maximize performance and where the

49
00:03:55,860 --> 00:04:01,080
consequences of errors are less severe, accuracy might be the primary focus.

50
00:04:01,560 --> 00:04:07,380
For instance, in recommendation systems for e-commerce platforms, highly accurate models can improve

51
00:04:07,410 --> 00:04:13,260
user experience by providing personalized recommendations, even if the underlying model is not readily

52
00:04:13,260 --> 00:04:14,160
interpretable.

53
00:04:15,720 --> 00:04:21,210
To address the trade off between accuracy and interpretability, researchers and practitioners have

54
00:04:21,210 --> 00:04:23,640
developed various techniques and tools.

55
00:04:24,150 --> 00:04:30,150
One approach is to use model agnostic interpretability methods, which can be applied to any machine

56
00:04:30,150 --> 00:04:32,730
learning model regardless of its complexity.

57
00:04:32,970 --> 00:04:39,090
Techniques such as local interpretable model, agnostic explanations and Shapley additive explanations

58
00:04:39,090 --> 00:04:45,120
provide insights into model predictions by approximating the behavior of complex models with simpler,

59
00:04:45,120 --> 00:04:46,590
interpretable models.

60
00:04:47,280 --> 00:04:53,550
Lime, for example, generates local explanations for individual predictions by approximating the complex

61
00:04:53,550 --> 00:04:57,030
model with a linear model in the vicinity of the prediction.

62
00:04:57,660 --> 00:05:02,550
Shap values, on the other hand, quantify the contribution of each feature to the prediction, Addiction,

63
00:05:02,580 --> 00:05:05,850
offering a comprehensive view of feature importance.

64
00:05:07,590 --> 00:05:13,800
Another approach is to design inherently interpretable models that achieve a balance between accuracy

65
00:05:13,800 --> 00:05:15,270
and interpretability.

66
00:05:15,990 --> 00:05:21,780
For instance, generalized additive models extend linear models by allowing non-linear relationships

67
00:05:21,780 --> 00:05:25,710
between predictors and the outcome while maintaining interpretability.

68
00:05:26,010 --> 00:05:32,160
These models can capture more complex patterns in the data compared to linear models, thereby improving

69
00:05:32,190 --> 00:05:35,340
accuracy without sacrificing transparency.

70
00:05:35,370 --> 00:05:41,580
Decision trees with constraints on depth and complexity or rule based models also offer interpretable

71
00:05:41,580 --> 00:05:45,960
solutions while providing reasonable accuracy in many applications.

72
00:05:47,070 --> 00:05:52,650
The choice of model also depends on the specific requirements and constraints of the AI project.

73
00:05:52,650 --> 00:05:58,470
For instance, regulatory requirements might mandate the use of interpretable models to ensure compliance

74
00:05:58,470 --> 00:06:00,000
with laws and standards.

75
00:06:00,450 --> 00:06:07,050
Ethical considerations such as fairness and bias mitigation might also necessitate the use of interpretable

76
00:06:07,050 --> 00:06:11,340
models to understand and address potential biases in predictions.

77
00:06:11,910 --> 00:06:18,330
Additionally, the target audience and end users of the AI system play a crucial role in model selection.

78
00:06:18,900 --> 00:06:24,570
If the end users are domain experts who require detailed explanations for decision making, interpretability

79
00:06:24,570 --> 00:06:26,160
becomes a key criterion.

80
00:06:26,520 --> 00:06:32,550
Conversely, if the end users are primarily concerned with the accuracy of predictions, a more complex,

81
00:06:32,580 --> 00:06:35,040
accurate model might be appropriate.

82
00:06:36,480 --> 00:06:43,110
Ultimately, the decision between accuracy and interpretability is not binary, but rather a spectrum

83
00:06:43,110 --> 00:06:47,010
where different models can be positioned based on their characteristics.

84
00:06:48,030 --> 00:06:53,100
Developing a clear understanding of the tradeoffs, as well as the tools and techniques available,

85
00:06:53,100 --> 00:06:59,460
enables AI governance professionals to make informed decisions that align with the goals and constraints

86
00:06:59,460 --> 00:07:00,690
of their projects.

87
00:07:01,470 --> 00:07:07,180
By carefully considering the context, regulatory environment, ethical implications, and end user

88
00:07:07,180 --> 00:07:07,840
needs.

89
00:07:07,840 --> 00:07:13,540
Practitioners can select models that not only perform well, but are also trustworthy and transparent.

90
00:07:14,830 --> 00:07:20,890
In conclusion, model selection is a critical aspect of the AI development life cycle, particularly

91
00:07:20,890 --> 00:07:26,590
in the planning phase where decisions about accuracy and interpretability can significantly impact the

92
00:07:26,590 --> 00:07:29,020
success and acceptance of AI systems.

93
00:07:29,560 --> 00:07:35,440
While high accuracy is often desirable for achieving optimal performance, interpretability is crucial

94
00:07:35,440 --> 00:07:39,280
for ensuring transparency, accountability, and user trust.

95
00:07:39,490 --> 00:07:45,490
Balancing these two dimensions requires a nuanced understanding of the tradeoffs and the application

96
00:07:45,490 --> 00:07:47,680
of appropriate techniques and tools.

97
00:07:48,100 --> 00:07:53,170
By navigating these complexities, AI governance professionals can develop models that are not only

98
00:07:53,170 --> 00:07:59,770
effective but also aligned with ethical and regulatory standards, ultimately contributing to the responsible

99
00:07:59,770 --> 00:08:02,860
and impactful deployment of AI technologies.