Explainable artificial intelligence: Difference between revisions

Revision as of 04:24, 18 July 2017

Revision as of 04:26, 18 July 2017

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{{AFC submission|||u=JonathanSinclair|ns=2|ts=20170717144255}} <!-- Do not remove this line! -->

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{{AFC comment|1=A review at [[WP:WikiProject Computing]] is in order. It isn't entirely clear what is meant by XAI; the article mentions that. [[User:Robert McClenon|Robert McClenon]] ([[User talk:Robert McClenon|talk]]) 04:26, 18 July 2017 (UTC)}}

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Since DARPA's introduction of it's program in 2016, a number of initiatives have started to address the issue of algorithmic accountability and provide transparency concerning how technologies within this ___domain function.

* 25.04.2017: Nvidia publishes it's paper on: "Explaining How a Deep Neural Network Trained with End-to-End Learning Steers a Car" <ref>{{cite web|title=Explaining How a Deep Neural Network Trained with End-to-End Learning Steers a Car|url=https://arxiv.org/pdf/1704.07911.pdf|website=Arxiv|publisher=Arxiv|accessdate=17 July 2017}}</ref>

* 13.07.2017: Accenture recommends, "Responsible AI: Why we need Explainable AI" <ref>{{cite web|title=Responsible AI: Why we need Explainable AI|url=https://www.youtube.com/watch?v=A668RoogabM|website=YouTube|publisher=Accenture|accessdate=17 July 2017}}</ref>

== Accountability ==

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Examples of these effects have already been seen in the following sectors:

* Neural Network Tank imaging <ref>{{cite web|title=Neual Network Tank image|url=https://neil.fraser.name/writing/tank/|website=Neil Fraser|publisher=Neil Fraser|accessdate=17 July 2017}}</ref>

* Antenna design ([[Evolved Antenna]]) <ref>{{cite web|title=NASA 'Evolutionary' software automatically designs antenna|url=https://www.nasa.gov/mission_pages/st-5/main/04-55AR.html|website=NASA|publisher=NASA|accessdate=17 July 2017}}</ref>

* Algorithmic trading ([[High-frequency trading]]) <ref>{{cite web|title=The Flash Crash: The Impact of High Frequency Trading on an Electronic Market|url=http://www.cftc.gov/idc/groups/public/@economicanalysis/documents/file/oce_flashcrash0314.pdf|website=CFTC|publisher=CFTC|accessdate=17 July 2017}}</ref>

* Medical diagnosis <ref>{{cite web|title=Can machine-learning improve cardiovascular risk prediction using routine clinical data?|url=http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0174944|website=PLOS One|publisher=PLOS One|accessdate=17 July 2017}}</ref>

* Autonomous vehicles <ref>{{cite web|title=Tesla says it has 'no way of knowing' if autopilot was used in fatal Chinese crash|url=https://www.theguardian.com/technology/2016/sep/14/tesla-fatal-crash-china-autopilot-gao-yaning|website=Guardian|publisher=Guardian|accessdate=17 July 2017}}</ref><ref>{{cite web|title=Joshua Brown, Who Died in Self-Driving Accident, Tested Limits of His Tesla|url=https://www.nytimes.com/2016/07/02/business/joshua-brown-technology-enthusiast-tested-the-limits-of-his-tesla.html|website=New York Times|publisher=New York Times|accessdate=17 July 2017}}</ref>

== Recent developments ==

As regulators, official bodies and general users dependency on AI-based dynamic systems, clearer accountability will be required for decision making processes to ensure trust and transparency. Evidence of this requirement gaining more momentum can be seen with the launch of the first global conference exclusively dedicated to this emerging discipline:

* International Joint Conference on Artificial Intelligence: Workshop on Explainable Artificial Intelligence (XAI) <ref>{{cite web|title=IJCAI 2017 Workshop on Explainable Artificial Intelligence (XAI)|url=http://home.earthlink.net/~dwaha/research/meetings/ijcai17-xai/|website=Earthlink|publisher=IJCAI |accessdate=17 July 2017}}</ref>

== External links ==

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{{DEFAULTSORT:Explainable AI}}

[[:Category:Artifical Intelligence]]

[[:Category:Autonomous Vehicles]]

[[:Category:XAI]]

[[:Category:Accountability]]

{{AFC submission|||ts=20170717144255|u=JonathanSinclair|ns=2}}