The Pentagon made headlines last month when it adopted its five principles for using artificial intelligence, marking the end of a months-long effort over what guidelines the department should follow as it develops new AI tools and AI-enabled technologies.<\/p>\n\n\n\n
Less well known is that the intelligence community is developing its own principles governing the use of AI.<\/p>\n\n\n\n
\u201cThe intelligence community has been doing it\u2019s own work in this space as well. We\u2019ve been doing it for quite a bit of time,\u201d Ben Huebner, chief of the Office of Director of National Intelligence\u2019s Civil Liberties, Privacy, and Transparency Office, said at an Intelligence and National Security Alliance event March 4.<\/p>\n\n\n\n
According to Huebner, ODNI is making progress in developing its own principles, although he did not give a timeline for when they would be officially adopted. They will be made public, he added, noting there likely wouldn\u2019t be any surprises.<\/p>\n\n\n\n
\u201cFundamentally, there\u2019s a lot of consensus here,\u201d said Huebner, who noted that ODNI had worked closely with the Department of Defense\u2019s Joint Artificial Intelligence Center on the issue.<\/p>\n\n\n\n
Key to the intelligence community\u2019s thinking is focusing on what is fundamentally new about AI.<\/p>\n\n\n\n
\u201cBluntly, there\u2019s a bit of hype,\u201d said Huebner. \u201cThere\u2019s a lot of things that the intelligence community has been doing for quite a bit of time. Automation isn\u2019t new. We\u2019ve been doing automation for decades. The amount of data that we\u2019re processing worldwide has grown exponentially, but having a process for handling data sets by the intelligence community is not new either.\u201d<\/p>\n\n\n\n
What is new is the use of machine learning for AI analytics. Instead of being explicitly programmed to perform a task, machine learning tools are fed data to train them to identify patterns or make inferences before being unleashed on real world problems. Because of this, the AI is constantly adapting or learning from each new bit of data it processes.<\/p>\n\n\n\n
That is fundamentally different from other IC analytics, which are static.<\/p>\n\n\n\n
\u201cWhy we need to sort of think about this from an ethical approach is that the government structures, the risk management approach that we have taken for our analytics, assumes one thing that is not true anymore. It generally assumes that the analytic is static,\u201d explained Huebner.<\/p>\n\n\n\n
To account for that difference, AI requires the intelligence community to think more about explainability and interpretability. Explainability is the concept of understanding how the analytic works, while interpretability is being able to understand a particular result produced by an analytic.<\/p>\n\n\n\n
\u201cIf we are providing intelligence to the president that is based on an AI analytic and he asks–as he does\u2014how do we know this, that is a question we have to be able to answer,\u201d said Huebner. \u201cWe\u2019re going to need to ensure that we have transparency and accountability in these structures as we use them. They have to be secure and resilient.\u201d<\/p>\n\n\n\n
ODNI is also building an ethical framework to help employees implement those principles in their daily work.<\/p>\n\n\n\n
\u201cThe thing that we\u2019re doing that we just haven\u2019t found an analog to in either the public or the private sector is what we\u2019re referring to as our ethical framework,\u201d said Huebner. \u201cThat drive for that came from our own data science development community, who said \u2018We care about these principles as much as you do. What do you actually want us to do?\u2019\u201d<\/p>\n\n\n\n
In other words, how do computer programmers apply these principles when they\u2019re actually writing lines of code? The framework won\u2019t provide all of the answers, said Huebner, but it will make sure employees are asking the right questions about ethics and AI.<\/p>\n\n\n\n
And because of the unique dynamic nature of AI analytics, the ethical framework needs to apply to the entire lifespan of these tools. That includes the training data being fed into them. After all, it\u2019s not hard to see how a data set with an underrepresented demographic could result in a higher error rate for that demographic than the population as a whole.<\/p>\n\n\n\n
\u201cIf you\u2019re going to use an analytic and it has a higher error rate for a particular population and you\u2019re going to be using it in a part of the world where that is the predominant population, we better know that,\u201d explained Huebner.<\/p>\n\n\n\n
The IC wants to avoid those biases due to concerns over privacy, civil liberties, and frankly, accuracy. And if biases are introduced into an analytic, intelligence briefers need to be able to explain that bias to policy makers so they can factor that into their decision making. That\u2019s part of the concepts of explainability and interpretability Huebner emphasized in his presentation.<\/p>\n\n\n\n
And because they are constantly changing, these analytics will require some sort of periodic review as well as a way to catalog the various iterations of the tool. After all, an analytic that was reliable a few months ago could change significantly after being fed enough new data, and not always for the better. The intelligence community will need to continually check the analytics to understand how they\u2019re changing and compensate.<\/p>\n\n\n\n
\u201cDoes that mean that we don\u2019t do artificial intelligence? Clearly no. But it means that we need to think about a little bit differently how we\u2019re going to sort of manage the risk and ensure that we\u2019re providing the accuracy and objectivity that we need to,\u201d said Huebner. \u201cThere\u2019s a lot of concern about trust in AI, explainability, and the related concept of interpretability.\u201d<\/p>\n","protected":false},"excerpt":{"rendered":"
While less public than the Pentagon’s Joint Artificial Intelligence Center, the intelligence community has been developing its own set of principles for the ethical use of artificial intelligence.<\/p>\n","protected":false},"author":7,"featured_media":62727,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"_yoast_wpseo_meta-robots-noindex":"","_yoast_wpseo_meta-robots-nofollow":"","_yoast_wpseo_canonical":"","_acf":"","_yoast_wpseo_primary_category":19,"_jetpack_feature_clip_id":0,"_jetpack_memberships_contains_paid_content":false,"footnotes":"","_smg_distribution_targets":[]},"categories":[160,36,19],"tags":[],"coauthors":[3385],"class_list":["post-28620","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-daily-news-roundup","category-newsletters","category-pentagon"],"acf":{"subheadline":"","legacy_arc_id":"WPJARCRI5ZGFHLSMRQWEAHMSAM","arc_canonical_url":"\/artificial-intelligence\/2020\/03\/06\/the-intelligence-community-is-developing-its-own-ai-ethics\/","remove_feature_photo":false,"is_sponsored":false,"subtype":"","redirect_url":"","disable_inline_ads":false,"native_logo_pretext":"Presented By:"},"yoast_head":"\n