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<p class="MsoNormal"><span style="font-size:11.0pt">The following paper got published in IEEE Access this week:
<a href="https://ieeexplore.ieee.org/document/11214385">Explainable AI (XAI) for Object Detection and Application to Satellite Imagery | IEEE Journals & Magazine | IEEE Xplore</a><o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:11.0pt"><o:p> </o:p></span></p>
<p class="MsoNormal"><span style="font-size:11.0pt">The current issue of Phalanx, a magazine of the Military Operations Research Society (<a href="https://urldefense.com/v3/__https:/urldefense.us/v2/url?u=https-3A__www.mors.org_&d=DwMFAg&c=MASr1KIcYm9UGIT-jfIzwQg1YBeAkaJoBtxV_4o83uQ&r=jmlOi54xwD7l3BRfyymQ6ZZiFOTtwVqWbKuOK_fLmbQ&m=Le9SsTcT-X3IsRvrPGUw4BbEM6hNbJNiD131CgZB7O8eS5O1UyMJ10PjeJYRMzGU&s=WvhAw6_UIkDtPKNFfy1jCkkIfzez255rmzPhz3R-e1s&e=__;!!IKRxdwAv5BmarQ!bqxdweUSfrMn0a7-hkdiqK6gDRxR0DQSlC7U2lgihgKkCsSejsAmXuNZkzkY2nQKelfxjEtfVdQ8ZfLfxriCUeIb7O7NDw$">Military
Operations Research Society > Home</a>) also has a featured article on this object recognition technology:
<a href="https://urldefense.com/v3/__https:/urldefense.us/v2/url?u=https-3A__www.mors.org_Portals_87_Documents_Publications_Phalanx_2025_Phalanx-5FSummer2025-5FWEB.pdf-3Fver-3DkMHxS6JM2kiWmOs3c6QgxA-253d-253d&d=DwMFAg&c=MASr1KIcYm9UGIT-jfIzwQg1YBeAkaJoBtxV_4o83uQ&r=jmlOi54xwD7l3BRfyymQ6ZZiFOTtwVqWbKuOK_fLmbQ&m=Le9SsTcT-X3IsRvrPGUw4BbEM6hNbJNiD131CgZB7O8eS5O1UyMJ10PjeJYRMzGU&s=iCsNk_qA89iQx2pi3D9sAY0mdaohiQZ1exVuGKQFZJk&e=__;!!IKRxdwAv5BmarQ!bqxdweUSfrMn0a7-hkdiqK6gDRxR0DQSlC7U2lgihgKkCsSejsAmXuNZkzkY2nQKelfxjEtfVdQ8ZfLfxriCUeLSTyEB4A$">
Phalanx_Summer2025_WEB.pdf</a><o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:11.0pt"><o:p> </o:p></span></p>
<p class="MsoNormal"><span style="font-size:11.0pt">The method is very simple. We build what DARPA had always wanted for Explainable AI (<a href="https://www.darpa.mil/research/programs/explainable-artificial-intelligence">Explainable Artificial Intelligence
| DARPA</a>) as far as object detection/image recognition goes: verify the parts of objects for explanation. We simply flipped the process of building models. We do it very much the way houses are designed and built based on the needs of the clients – you
meet with the clients first to figure out what exactly they want in the new house. The result of this process of building explainable models has surprising benefits, beyond just the explanation:<o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:11.0pt"><o:p> </o:p></span></p>
<ol style="margin-top:0in" start="1" type="1">
<li class="MsoListParagraph" style="margin-left:0in;mso-list:l3 level1 lfo3"><span style="font-size:11.0pt">Higher accuracy than a standard, non-XAI model, and<o:p></o:p></span></li><li class="MsoListParagraph" style="margin-left:0in;mso-list:l3 level1 lfo3"><span style="font-size:11.0pt">Protection against adversarial attacks without any adversarial training.
<o:p></o:p></span></li></ol>
<p class="MsoNormal"><span style="font-size:11.0pt"><o:p> </o:p></span></p>
<p class="MsoNormal"><span style="font-size:11.0pt">Other features, things to note:<o:p></o:p></span></p>
<ol style="margin-top:0in" start="1" type="a">
<li class="MsoListParagraph" style="margin-left:0in;mso-list:l1 level1 lfo6"><span style="font-size:11.0pt">It’s a Neuro-Symbolic System - Ought to mention that a layer of symbol processing on top of a complex pattern recognition machine works like a charm
and you get the benefits of higher accuracy and adversarial attack protection. Don’t need any adversarial training.<o:p></o:p></span></li><li class="MsoListParagraph" style="margin-left:0in;mso-list:l1 level1 lfo6"><span style="font-size:11.0pt">The EU AI Act - This approach to explainability will make it easy to satisfy the provisions of the EU AI Act (<a href="https://artificialintelligenceact.eu/the-act/">The
Act Texts | EU Artificial Intelligence Act</a>) which requires model explainability.<o:p></o:p></span></li></ol>
<p class="MsoNormal"><span style="font-size:11.0pt"><o:p> </o:p></span></p>
<p class="MsoNormal"><span style="font-size:11.0pt">Much of the inspiration and motivation for this approach came from late Prof. Horace Barlow of Cambridge University (<a href="https://en.wikipedia.org/wiki/Horace_Barlow">Horace Barlow - Wikipedia</a>), the
great-grandson of Charles Darwin, who in his later years became a “<i>single unit freak</i>” and a proponent of the grandmother cell theory. He was one of the pioneer vision scientists and received many international awards. In his words on grandmother cells:
“</span><i>though I still hold that something conforming amazingly well to what was conceptualised by Jerry Lettvin 50 years ago, really does exist</i>!...
<i>and of course the more widely discussed it is the better pleased I shall be, though I fear that what I have written will not be universally accepted, at least at first</i>!” That single cell theory actually gets us to abstract concepts and symbolic reasoning
beyond pattern recognition. And we have plenty of neurophysiological evidence for single cell abstractions in the brain.<o:p></o:p></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal">We are interested in exploring the application of these ideas for computer vision systems that are considered to be high-risk by the EU AI Act. Please feel free to contact me if there’s interest in building actual applications.
<o:p></o:p></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal">Asim Roy<o:p></o:p></p>
<p class="MsoNormal">Professor, Information Systems<o:p></o:p></p>
<p class="MsoNormal">Arizona State University<o:p></o:p></p>
<p class="MsoNormal"><span style="font-size:11.0pt"><a href="https://search.asu.edu/profile/9973">Asim Roy | ASU Search</a><o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:11.0pt"><o:p> </o:p></span></p>
<p class="MsoNormal"><span style="font-size:11.0pt;mso-ligatures:none"><img border="0" width="452" height="367" style="width:4.7083in;height:3.8229in" id="_x0000_i1026" src="cid:image003.jpg@01DC447D.660CFC00" alt="Inside Darpa's Push to Make Artificial Intelligence Explain Itself - WSJ"></span><span style="font-size:11.0pt">
</span><span style="mso-ligatures:none"><img border="0" width="499" height="364" style="width:5.1979in;height:3.7916in" id="Picture_x0020_1" src="cid:image004.jpg@01DC448B.3D82AFB0"></span><span style="font-size:11.0pt"><o:p></o:p></span></p>
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