When algorithms replace humans, what is at stake? 

Com­put­er sys­tems are increas­ing­ly involved in every­day deci­sions, espe­cial­ly arti­fi­cial intel­li­gence (AI), which can take over func­tions pre­vi­ous­ly per­formed by humans. But how can we trust them if we do not know how they work? And what about when this sys­tem is called upon to make deci­sions that could put our lives at stake? 

Véronique Stey­er, lec­tur­er in the Man­age­ment of Inno­va­tion and Entre­pre­neur­ship (MIE) depart­ment at École Poly­tech­nique (IP Paris), has been work­ing on the ques­tion of the explic­a­bil­i­ty of AI for sev­er­al years. Accord­ing to her, it is impor­tant to dis­tin­guish the notion of inter­pretabil­i­ty – which con­sists of under­stand­ing how an algo­rithm works, to improve its robust­ness and diag­nose its flaws – from the notion of account­abil­i­ty. This rais­es the ques­tion: in the event of mate­r­i­al or phys­i­cal dam­age caused by an arti­fi­cial intel­li­gence, what is the degree of respon­si­bil­i­ty of the per­son or com­pa­ny that designed or uses this AI? 

How­ev­er, when they exist, AI explain­abil­i­ty tools are gen­er­al­ly devel­oped with an inter­pretabil­i­ty log­ic, and not with an account­abil­i­ty log­ic. To put it plain­ly, they allow us to observe what is hap­pen­ing in the sys­tem, with­out nec­es­sar­i­ly try­ing to under­stand what is going on, and which deci­sions are tak­en accord­ing to which cri­te­ria. They can thus be good indi­ca­tors of the degree of per­for­mance of the AI, with­out assur­ing the user of the rel­e­vance of the deci­sions taken. 

For her, it is there­fore nec­es­sary to pro­vide a reg­u­la­to­ry frame­work for these AIs. In France, the pub­lic health code already stip­u­lates that “the design­ers of an algo­rith­mic treat­ment […] ensure that its oper­a­tion is explic­a­ble for users” (law n° 2021–1017 of 2^nd August 2021). With this text, the leg­is­la­tor was aim­ing more specif­i­cal­ly at AIs used to diag­nose cer­tain dis­eases, includ­ing can­cers. But it is still nec­es­sary to train users – in this case health pro­fes­sion­als – not only in AI, but also in the func­tion­ing and inter­pre­ta­tion of AI expla­na­tion tools… How else will we know if the diag­no­sis is made accord­ing to the right criteria? 

At the Euro­pean lev­el, a draft reg­u­la­tion is under­way in 2023, which should lead to the clas­si­fi­ca­tion of AIs accord­ing to dif­fer­ent lev­els of risk, and to require a cer­ti­fi­ca­tion that would guar­an­tee var­i­ous degrees of explain­abil­i­ty. But who should devel­op these tools, and how can we pre­vent the GAFAs from con­trol­ling them? “We are far from hav­ing answered all these thorny ques­tions, and many com­pa­nies that devel­op AI are still unaware of the notion of explic­a­bil­i­ty, » notes Véronique Steyer. 

Mean­while, increas­ing­ly pow­er­ful AIs are being devel­oped in more and more diverse sec­tors of activ­i­ty. An entre­pre­neur in AI, Milie Taing found­ed the start-up Lili​.ai in 2016 on the Poly­tech­nique cam­pus. She first spent eight years as a project man­ag­er, spe­cial­is­ing in cost con­trol, at SNC Lavalin, the Cana­di­an leader in large projects. It was there that she had to trace the his­to­ry of sev­er­al major con­struc­tion projects that had fall­en far behind schedule. 

To doc­u­ment com­plaints, it was nec­es­sary to dig through up to 18 years of very het­ero­ge­neous data (email exchanges, attach­ments, meet­ing min­utes, etc.) and to iden­ti­fy when the errors explain­ing the delay in these projects had occurred. But it is impos­si­ble for humans to analyse data scat­tered over thou­sands of mail­box­es and decades. In large con­struc­tion projects, this doc­u­men­ta­tion chaos can lead to very heavy penal­ties, and some­times even bank­rupt­cy. Milie Taing there­fore had the idea of team­ing up with data sci­en­tists and devel­op­ers to devel­op an arti­fi­cial intel­li­gence soft­ware whose role is to car­ry out doc­u­men­tary archaeology.

“To explore the past of a project, our algo­rithms open all the doc­u­ments relat­ed to that project one by one. Then they extract all the sen­tences and key­words and auto­mat­i­cal­ly tag them with hash­tags, a bit like Twit­ter,” explains Milie Taing. These hash­tags ulti­mate­ly make it pos­si­ble to con­duct lit­er­a­ture search­es effi­cient­ly. To avoid prob­lems in the case of an ongo­ing project, we have mod­elled a hun­dred or so recur­ring prob­lems that could lead to a com­plaint and pos­si­ble penalties.

Lili.ai’s soft­ware is already used by major accounts such as the Société du Grand Paris, EDF and Ora­no (nuclear pow­er plant man­age­ment). And accord­ing to Milie Taing, it does not threat­en the jobs of project man­agers. “In this case, AI assists in the man­age­ment of prob­lems, which makes it pos­si­ble to iden­ti­fy mal­func­tions before it’s too late,” she says. It aims to free humans from time-con­sum­ing and repet­i­tive tasks, allow­ing them to con­cen­trate on high­er val­ue-added work. 

AI aims to free humans from time-con­sum­ing tasks, allow­ing them to con­cen­trate on high­er val­ue-added work.

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But doesn’t this AI risk point out the respon­si­bil­i­ty, or even the guilt, of cer­tain peo­ple in the fail­ure of a project? “In fact, employ­ees are attached to their projects and are pre­pared to give away their e‑mails to recov­er the costs and mar­gin that would have been lost if the work was delayed. Although, legal­ly, staff e‑mails belong to the com­pa­ny, we have includ­ed very sophis­ti­cat­ed fil­ter­ing func­tions in our soft­ware that give employ­ees con­trol over what they do or do not accept to export in the Lili solu­tion,” she states. 

Accord­ing to Milie Taing, it is in France’s inter­est to invest in this type of AI, as it has some of the best inter­na­tion­al exper­tise in the exe­cu­tion of very large projects, and there­fore has access to colos­sal amounts of data. On the oth­er hand, it will be less effi­cient than the Asians, for exam­ple, in oth­er appli­ca­tions, such as facial recog­ni­tion, which more­over goes against a cer­tain French ethic. 

“All tech­nol­o­gy car­ries a script, with what it does or does not allow us to do, the role it gives to humans, and the val­ues it car­ries,” Véronique Stey­er points out. “For exam­ple, in the 1950s, in Cal­i­for­nia, a road lead­ing to a beach was built, and to pre­vent the beach from being invad­ed by a pop­u­la­tion of too mod­est ori­gin, the bridges span­ning the road were set very low, which pre­vent­ed bus­es from pass­ing. So, I think it’s very impor­tant to under­stand not only how a sys­tem works, but also what soci­etal choic­es are embed­ded in an AI sys­tem in a total­ly tac­it way that we don’t see.”

Cur­rent­ly, the most wide­spread AIs are chat­bots, which can­not be said to threat­en the human species. But by becom­ing accus­tomed to the per­for­mance of these chat­bots, we could tomor­row neglect to ques­tion the mech­a­nisms and objec­tives of more sophis­ti­cat­ed AI. 

Marina Julienne