Questions and Answers
Is 100% fairness even possible? If not, what is an acceptable metric?
100% fairness is not possible by any definition of 100% fairness. You’re not going to have a metric that 100% of people find fair. You’re not going to have a model that will be guaranteed fair in 100% of the cases. This is for a few reasons, but a big one is that there are many plausible and even compelling definitions of fairness. In many realistic scenarios, it’s not possible to satisfy all such cases.
Re: acceptable metric. This will depend on a host of factors, including which kinds of mistakes are most worrying, whether the stakes are high or low, whether there are historical reasons to not just go for formal equality but to even favor equality-enhancing measures, and so on. So for example, I would say that in the case of a life-saving medical application (high stakes), the right fairness metric might not be the same as the case of legal price discrimination (that is where retailers might price goods differently based on their belief that some customers are willing to pay more for the same good).
Is 100% secure data possible (on the technical side)?
This is also a no. I am not a data security expert, but based on what I do know, there are lots of “known unknowns” (such as the extent of joinable data already out in the world, or the extent of vulnerability of a given ML model to certain adversarial attacks). There are also presumably unknown unknowns. So yeah, no computer security expert is going to promise 100% data security. This is especially true because humans ultimately have some form of access at some point. For example, even in end-to-end encrypted applications, there are easy data leaks, such as a friend screenshotting and sharing pictures of conversations.
Is there any trade off between fairness and security?
This is an interesting one. I am going to assume that when people ask about fairness, they are largely referring to the specific fairness concerns of equality and anti-discrimination. In such a case, empirically trade offs have been observed. For example, I have seen papers (not difficult to find if you look) where training models to be more robust to adversarial attacks tends to enhance unfairness as indicated by a variety of fairness metrics (such as differential error rates between groups).
In general, with most technical systems, it’s safe to assume there are trade-offs almost always (unless proven otherwise). However these trade-offs can sometimes be minuscule and not of true concern even if they do exist.
Hello Aileen Nielsen!
Thank you for your time! The topic your book covers is definetely in demand.
May I ask some questions that are not only technical:
- What is your view on changing the existing language in order to shift systematical thinking towards more acceptable and fair? (For example, in some languages that have gender distinctiveness, people try to add new endings specific ending to female professionals etc.)
- Do we always try to identify bias and create models by excluding it or sometimes it’s important to keep it? When then?
In answer to your first question, I am all for making language more inclusive (and precise). So I find the idea of modernizing language use, grammar, etc, to be more inclusive to be a great idea. I also think it’s good to call out language that has long been accepted but that actually perpetuates inequality. For example, it’s still common for people to say things like “so easy your grandma could do it” or “how would you explain this to your grandma?”. “Grandma” (but not grandpa) tends to be the stand-in for a presumed doddering old person. This is both sexist and ageist, so I try to call this out.
On the other hand, I’m not sure it’s always productive to be the “thought police” and if people don’t feel that they can speak freely without getting attacked, that can lead to its own problems. So yes, in general I am all for more inclusive language, but I am also for a welcoming environment for all and taking care not to become the “thought police”
But these are my own personal opinions and not related to the topic of coding for fairness so don’t take these as anything more than my own thoughts 🙂
Regarding your second question, whether bias should always be identified and removed, in theory I would say yes. A perfect world would be one where we can get rid of all bias. However, in practice, I think for the moment it’s best to focus on high stakes applications where the real costs to humans of bias are quite high. So I am far more worried about bias potentially baked in to ML models built for policing or healthcare or education than I am about ML models built for retail or gaming. (Also see my earlier question that what constitutes fairness or bias has many definitions, so that it won’t always be possible for everyone to agree on whether bias has been removed).
hi, Aileen Nielsen
As biasness are often detected in production (and usually with material impact):
- Are there ways to catch them before that?
- Would the model(s) need to be explainable?
Also, are models that use online learning susceptible to biasness and even insecurity over time?
One last question: is fairness and security equally applicable to unsupervised learning? If so, how?
Re: q1: In my opinion, bias is detected in production models for two reasons. (1) No work has been done to debias/audit that particular model (surprisingly common) and (2) Some work has been done, but the people identifying the bias may have a different definition of bias or a different threshold for establishing bias.
But yes there are absolutely ways to audit a model for bias before it is rolled out into production. There are in fact many ways to do so (and more than one way can be used). For starters, you can preprocess the data to remove bias you identify in the data, you can in process by adding fairness awareness to your training process, or you can post process to modify model outputs to make them more conformant to a fairness definition. You don’t have to do just one of these things….you can do them all.
Re: question of explainability. Do models need to be explainable? This is a hot topic, and I certainly think that explanation can be appropriate and even necessary in many domains. On the other hand, I sometimes think explainability is overemphasized in some discussions. For example, we should also recognize that in human decision making there is a lot that goes unexplained (or where explanations can be pretextual rather than true). Therefore I am a big believer in explainability generally but I do not see this as necessary or even desirable in all cases. In fact, sometimes explanations can be more confusing than helpful.
Regarding your questions about online learning and unsupervised learning. Yes, there are absolutely security and fairness concerns here as well. These elements of ML are less used in industry, so that’s one reason I focus on them less. Also there way to make such practices more fair and robust are also even less clear than in the case of supervised ML. However, a good example of a real world scenario where such a model existed and rapidly went bad is of course Microsoft’s Tay chatbot: https://www.theverge.com/2016/3/24/11297050/tay-microsoft-chatbot-racist
Woah, thanks for such thoughtful responses! Sorry for such late reply, ‘cos I had some urgent family matters to attend to.
Aileen Nielsen: Thank you for being here. Fairness of AI systems is an important topic. What metrics can be used to measure the fairness level of an AI system? What needs to be standardized between two AI systems before we can compare their fairness levels (so that it’s apples-to-apples comparison)? And what levers are available to influence the fairness level of an AI system?
Good questions. Regarding metrics, there are SO many fairness metrics. A good starting point to get a sense of some commonly used metrics but also to understand some of the nuances and social influences is Arvind Narayanan’s tutorial on this topic: https://fairmlbook.org/tutorial2.html (21 fairness definitions and their politics)
Also I think your second question about apples to apples comparisons is super interesting. I would say that models trained for different purposes are not comparable. For example, is there any sense in which I can say that a particular medical AI application is fairer than some ML credit scoring algorithm? I think not. For example, the consequences of making a mistake are quite different (e.g. death from wrong treatment versus not getting a loan won’t kill you). Also the historical considerations may be different (for example, there is evidence that minorities continue to be discriminated against in the case of credit….but I think this is less problematic for women. On the other hand in the case of medicine there is substantial evidence that minorities but also women face substantial discrimination in the quality of medical care they receive).
So I think it’s an interesting question but I’m not sure any simple apples to apples comparison is achievable.
Aileen Nielsen Thank you for the answers.
Hi everyone! Thanks a lot for your questions. I’ll be answering in individual threads to the original poster of each question.
Hi Aileen Nielsen, thanks for your time.
- In your opinion, what are the biggest security risks a data scientist should be aware of?
- Is there a general procedure on how to test if my models are discriminating?
Re q1: I think a lack of precautions regarding protecting identifying information is the biggest problem I have seen in my experiences in a variety of organizations. This presents itself in a variety of ways. Sometimes identifying information is gratuitously made available when people are modeling even though it is in no way necessary. Relatedly, information is not segmented to minimize what is available to any given user (privacy by design). Finally, most data scientists I have worked with do not seem to be aware or take seriously the danger of reidentification even when identifying information has been putatively removed.
Re q2: There are many general procedures but there is no one single procedure. Two good resources to get you started are two toolkits. (1) http://aequitas.dssg.io and (2) https://aif360.mybluemix.net
I discuss both of these toolkits in my book, but you can also easily learn them through the many tutorial examples they give.
thank you :)
Hi Aileen Nielsen thanks so much for writing this book and for your time here. I’m quite new to AI fairness and I still find it hard to wrap my head around its framework / toolkits.
In particular, I notice the topic of AI fairness is mostly wrapped around data/models that involve users/groups of people etc. Personally, I work mostly around data from machinery sensors and engineering data in the energy sector, a lot of which do not have direct implications to any particular groups of people. Will AI fairness still be applicable to my data and models, or am I amiss in even asking such question? In general, how do I know if AI fairness applicable to my work / domain?
Absolutely! sensor data is actually incredibly sensitive from a privacy perspective, and I’m a firm believer that privacy is a core pillar of algorithmic fairness
Consider for example this law review article that describes why energy usage is in fact quite sensitive and should be protected: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3667125
Also to the extent that you view fairness through an equality lens, I can imagine many ways in which sensor data in the energy sector may have unequal impacts. For example are communities of color disproportionately subjected to highly granular data collection of energy?
awesome thank you so much for your input. A lot to chew on for sure, but nevertheless a very essential and much needed topic for a time such as this!
Also apologize for creating another thread, but I have a different question: for all the AI fairness metrics that are available out there, what is the ground basis for fairness? How does one define “fairness”?
There is no single definition of “fairness”. In fact, this is something you’ll find varies between cultures and between individuals
Given my legal training, I tend to focus on the most fundamental definitions of fairness, which tend to relate to antidiscrimination (equality) and to fundamental rights (fair process). In these cases, there are decades of legal guidance, and so I tend to think a good start is to - at the least - make sure that algorithmic fairness in domains regulated by law is at least meeting the same standards that have been in place for decades
but more generally the question of what is fair is not a question anyone can answer definitively
therefore for productive ML work I think the best is to consider the fairness considerations of a particular domain and then to think about what fairness metric makes sense for that domain
okay that’s pretty clear - sounds like one needs to evaluate the context of the region and cultures etc.