82. Bias and Machine Learning

[Editor’s Note:  Today’s post poses four central questions to our Mad Scientist community of action regarding bias in machine learning and the associated ramifications for artificial intelligence, autonomy, lethality, and decision-making on future warfighting.]

We thought that we had the answers, it was the questions we had wrong” – Bono, U2

Source: www.vpnsrus.com via flickr

As machine learning and deep learning algorithms become more commonplace, it is clear that the utopian ideal of a bias-neutral Artificial Intelligence (AI) is exactly just that. These algorithms have underlying biases embedded in their coding, imparted by their human programmers (either consciously or unconsciously). These algorithms can develop further biases during the machine learning and training process.  Dr. Tolga Bolukbasi, Boston University, recently described algorithms as not being capable of distinguishing right from wrong, unlike humans that can judge their actions, even when they act against ethical norms. For algorithms, data is the ultimate determining factor.

Realizing that algorithms supporting future Intelligence, Surveillance, and Reconnaissance (ISR) networks and Commander’s decision support aids will have inherent biases — what is the impact on future warfighting? This question is exceptionally relevant as Soldiers and Leaders consider the influence of biases in man-machine relationships, and their potential ramifications on the battlefield, especially with regard to the rules of engagement (i.e., mission execution and combat efficiency versus the proportional use of force and minimizing civilian casualties and collateral damage).

It is difficult to make predictions, particularly about the future.” This quote has been attributed to anyone ranging from Mark Twain to Niels Bohr to Yogi Berra. Point prediction is a sucker’s bet. However, asking the right questions about biases in AI is incredibly important.

The Mad Scientist Initiative has developed a series of questions to help frame the discussion regarding what biases we are willing to accept and in what cases they will be acceptable. Feel free to share your observations and questions in the comments section of this blog post (below) or email them to us at:  usarmy.jble.tradoc.mbx.army-mad-scientist@mail.mil.

1) What types of bias are we willing to accept? Will a so-called cognitive bias that forgoes a logical, deliberative process be allowable? What about a programming bias that is discriminative towards any specific gender(s), ethnicity(ies), race(s), or even age(s)?

2) In what types of systems will we accept biases? Will machine learning applications in supposedly non-lethal warfighting functions like sustainment, protection, and intelligence be given more leeway with regards to bias?

3) Will the biases in machine learning programming and algorithms be more apparent and/or outweigh the inherent biases of humans-in-the-loop? How will perceived biases affect trust and reliance on machine learning applications?

4) At what point will the pace of innovation and introduction of this technology on the battlefield by our adversaries cause us to forego concerns of bias and rapidly field systems to gain a decisive Observe, Orient, Decide, and Act (OODA) loop and combat speed advantage on the Hyperactive Battlefield?

For additional information impacting on this important discussion, please see the following:

An Appropriate Level of Trust… blog post

Ethical Dilemmas of Future Warfare blog post

Ethics and the Future of War panel discussion video

One Reply to “82. Bias and Machine Learning”

  1. Dr. Lydia Kostopoulos posted about this blog and Linked-In and suggested the community of interest comment. Great set of questions that raise more questions.
    The first question that comes to mind is how do you define ‘bias’ in the realm of war fighting? Do you use the societal norms of your host nation or organization or do you tailor your ML/AI approach to the adversary? What is bias when the adversary wants to destroy your nation, your way of life, and you? Consider the Global War on Terror (GWOT) for example. Do we label the data based on what we know (demographics, culture, language, equipment, tactics, etc) about the adversary (Al Qaeda, ISIS, Boko Haram, Al Shabaab, The Taliban, Haqqani Network, etc) in order to train the models? Contrast a GWOT adversary with a near-peer nation state adversary and how we would collect and label the data and train the models. How do we control risk to friendly forces in the model if we potentially introduce constraints due to actual or perceived biases? Look forward to the discussion.

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