If the original purpose for developing AI was to help humans in some tasks and that purpose still holds, why should we care about its explainability? For example, in deep learning, as long as the intelligence helps us to the best of their abilities and carefully arrives at its decisions, why would we need to know how its intelligence works?
As argued by Selvaraju et al., there are three stages of AI evolution, in all of which interpretability is helpful.
In the early stages of AI development, when AI is weaker than human performance, transparency can help us build better models. It can give a better understanding of how a model works and helps us answer several key questions. For example why a model works in some cases and doesn't in others, why some examples confuse the model more than others, why these types of models work and the others don't, etc.
When AI is on par with human performance and ML models are starting to be deployed in several industries, it can help build trust for these models. I'll elaborate a bit on this later, because I think that it is the most important reason.
When AI significantly outperforms humans (e.g. AI playing chess or Go), it can help with machine teaching (i.e. learning from the machine on how to improve human performance on that specific task).
Why is trust so important?
First, let me give you a couple of examples of industries where trust is paramount:
In healthcare, imagine a Deep Neural Net performing diagnosis for a specific disease. A classic black box NN would just output a binary "yes" or "no". Even if it could outperform humans in sheer predictability, it would be utterly useless in practice. What if the doctor disagreed with the model's assessment, shouldn't he know why the model made that prediction; maybe it saw something the doctor missed. Furthermore, if it made a misdiagnosis (e.g. a sick person was classified as healthy and didn't get the proper treatment), who would take responsibility: the model's user? the hospital? the company that designed the model? The legal framework surrounding this is a bit blurry.
Another example are self-driving cars. The same questions arise: if a car crashes whose fault is it: the driver's? the car manufacturer's? the company that designed the AI? Legal accountability, is key for the development of this industry.
In fact, this lack of trust, has according to many hindered the adoption of AI in many fields (sources: 1, 2, 3). While there is a running hypothesis that with more transparent, interpretable or explainable systems users will be better equipped to understand and therefore trust the intelligent agents (sources: 1, 2, 3).
In several real world applications you can't just say "it works 94% of the time". You might also need to provide a justification...
Several governments are slowly proceeding to regulate AI and transparency seems to be at the center of all of this.
The first to move in this direction is the EU, which has set several guidelines where they state that AI should be transparent (sources: 1, 2, 3). For instance the GDPR states that if a person's data has been subject to "automated decision-making" or "profiling" systems, then he has a right to access
"meaningful information about the logic involved"
Now this is a bit blurry, but there is clearly the intent of requiring some form of explainability from these systems. The general idea the EU is trying to pass is that "if you have an automated decision-making system affecting people's lives then they have a right to know why a certain decision has been made." For example a bank has an AI accepting and declining loan applications, then the applicants have a right to know why their application was rejected.
To sum up...
Explainable AIs are necessary because:
- It gives us a better understanding, which helps us improve them.
- In some cases we can learn from AI how to make better decisions in some tasks.
- It helps users trust AI, which which leads to a wider adoption of AI.
- Deployed AIs in the (not to distant) future might be required to be more "transparent".
Why do we need explainable AI? ... why we need to know "how does its intelligence work?"
Because anyone with access to the equipment, enough skill, and enough time, can force the system to make a decision that is unexpected. The owner of the equipment, or 3rd parties, relying on the decision without an explanation as to why it is correct would be at a disadvantage.
Examples - Someone might discover:
People whom are named John Smith and request heart surgery on: Tuesday mornings, Wednesday afternoons, or Fridays on odd days and months have a 90% chance of moving to the front of the line.
Couples whom have the male's last name an odd letter in the first half of the alphabet and apply for a loan with a spouse whose first name begins with a letter from the beginning of the alphabet are 40% more likely to receive the loan if they have fewer than 5 bad entries in their credit history.
Notice that the above examples ought not to be determining factors in regards to the question being asked, yet it's possible for an adversary (with their own equipment, or knowledge of the algorithm) to exploit it.
"AdvHat: Real-world adversarial attack on ArcFace Face ID system" (Aug 23 2019) by Stepan Komkov and Aleksandr Petiushko
- Creating a sticker and placing it on your hat fools facial recognition system.
"Defending against Adversarial Attacks through Resilient Feature Regeneration" (Jun 8 2019), by Tejas Borkar, Felix Heide, and Lina Karam
"Deep neural network (DNN) predictions have been shown to be vulnerable to carefully crafted adversarial perturbations. Specifically, so-called universal adversarial perturbations are image-agnostic perturbations that can be added to any image and can fool a target network into making erroneous predictions. Departing from existing adversarial defense strategies, which work in the image domain, we present a novel defense which operates in the DNN feature domain and effectively defends against such universal adversarial attacks. Our approach identifies pre-trained convolutional features that are most vulnerable to adversarial noise and deploys defender units which transform (regenerate) these DNN filter activations into noise-resilient features, guarding against unseen adversarial perturbations.".
"One pixel attack for fooling deep neural networks" (May 3 2019), by Jiawei Su, Danilo Vasconcellos Vargas, and Sakurai Kouichi
- Altering one pixel can cause these errors:
Fig. 1. One-pixel attacks created with the proposed algorithm that successfully fooled three types of DNNs trained on CIFAR-10 dataset: The All convolutional network (AllConv), Network in network (NiN) and VGG. The original class labels are in black color while the target class labels and the corresponding confidence are given below.
Fig. 2. One-pixel attacks on ImageNet dataset where the modified pixels are highlighted with red circles. The original class labels are in black color while the target class labels and their corresponding confidence are given below.
Without an explanation as to how and why a decision is arrived at the decision can't be absolutely relied upon.
If you're a bank, hospital or any other entity that uses predictive analytics to make a decision about actions that have huge impact on people's lives, you would not make important decisions just because Gradient Boosted trees told you to do so. Firstly, because it's risky and the underlying model might be wrong and, secondly, because in some cases it is illegal - see Right to explanation.
Explainable AI is often desirable because
AI (in particular, artificial neural networks) can catastrophically fail to do their intended job. More specifically, it can be hacked or attacked with adversarial examples or it can take unexpected wrong decisions whose consequences are catastrophic (for example, it can lead to the death of people). For instance, imagine that an AI is responsible for determining the dosage of a drug that needs to be given to a patient, based on the conditions of the patient. What if the AI makes a wrong prediction and this leads to the death of the patient? Who will be responsible for such an action? In order to accept the dosage prediction of the AI, the doctors need to trust the AI, but trust only comes with understanding, which requires an explanation. So, to avoid such possible failures, it is fundamental to understand the inner workings of the AI, so that it does not make those wrong decisions again.
AI often needs to interact with humans, which are sentient beings (we have feelings) and that often need an explanation or reassurance (regarding some topic or event).
In general, humans are often looking for an explanation and understanding of their surroundings and the world. By nature, we are curious and exploratory beings. Why does an apple fall?
The answer to this is incredibly simple. If you are a bank executive one day you may need to stand up in court and explain why your AI denied mortgages to all these people... who just happen to share some protected characteristic under anti-discrimination legislation. The judge will not be happy if you handwave the question away mumbling something about algorithms. Or worse, why did this car/plane crash and how will you prevent it next time.
This is the major blocker to more widespread adoption of AI in many industries.
In addition to all these answers mentioning the more practical reasons of why we'd want explainable AIs, I'd like to add a more philosophical one.
Understanding how things around us work is one of the main driving forces of science from antiquity. If you don't have an understanding of how things work, you can't evolve beyond that point. Just because "gravity works" hasn't stopped us trying to understand how it works. In turn a better understanding of it led to several key discoveries, which have helped us advance our technology.
Likewise, if we stop at "it works" we will stop improving it.
AI hasn't been just about making "machines think", but also through them to understand how the human brain works. AI and neuroscience go hand-by-hand.
This all wouldn't be possible without being able to explain AI.