Category: Articles

Towards a realist AI design practice?

This is a ver­sion of the open­ing state­ment I con­tributed to the pan­el “Evolv­ing Per­spec­tives on AI and Design” at the Design & AI sym­po­sium that was part of Dutch Design Week 2024. I had the plea­sure of join­ing Iohan­na Nicen­boim and Jesse Ben­jamin on stage to explore what could be called the post-GenAI pos­si­bil­i­ty space for design. Thanks also to Math­ias Funk for moderating. 

The slide I displayed:

My state­ment:

  1. There’s a lot of mag­i­cal think­ing in the AI field today. It assumes intel­li­gence is latent in the struc­ture of the inter­net. Metaphors like AGI and super­in­tel­li­gence are mag­i­cal in nature. AI prac­tice is also very secre­tive. It relies on demon­stra­tions. This leads to a lack of rig­or and polit­i­cal account­abil­i­ty (cf. Gilbert & Lam­bert in Ven­ture­Beat, 2023).
  2. Design in its ide­al­ist mode is eas­i­ly fooled by such mag­ic. For exam­ple, in a recent report, the Dutch Court of Audit states that 35% of gov­ern­ment AI sys­tems are not known to meet expec­ta­tions (cf. Raji et al., 2022).
  3. What is need­ed is design in a real­ist mode. Real­ism focus­es on who does what to whom in whose inter­est (cf. Geuss, 2008, 23 in von Busch & Palmås, 2023). Applied to AI the ques­tion becomes who gets to do AI to whom? This isn’t to say we should con­sid­er AI tech­nolo­gies com­plete­ly inert. It medi­ates our being in the world (Ver­beek, 2021). But we should also not con­sid­er it an inde­pen­dent force that’s just drag­ging us along.
  4. The chal­lenge is to steer a path between, on the one hand, whole­sale cyn­i­cal rejec­tion and naive, opti­mistic, uncon­di­tion­al embrace, on the oth­er hand.
  5. In my own work, what that looks like is to use design to make things that allow me to go into sit­u­a­tions where peo­ple are build­ing and using AI sys­tems. And to use those things as instru­ments to ask ques­tions relat­ed to human auton­o­my, social con­trol, and col­lec­tive free­dom in the face of AI.
  6. The exam­ple shown is an ani­mat­ed short depict­ing a design fic­tion sce­nario involv­ing intel­li­gent cam­era cars used for pol­i­cy exe­cu­tion in urban pub­lic space. I used this video to talk to civ­il ser­vants about the chal­lenges fac­ing gov­ern­ments who want to ensure cit­i­zens remain in con­trol of the AI sys­tems they deploy (cf. Alfrink et al., 2023).
  7. Why is this real­ist? Because the work looks at how some groups of peo­ple use par­tic­u­lar forms of actu­al­ly exist­ing AI to do things to oth­er peo­ple. The work also fore­grounds the com­pet­ing inter­ests that are at stake. And it frames AI as nei­ther ful­ly autonomous nor ful­ly pas­sive, but as a thing that medi­ates peo­ples’ per­cep­tions and actions.
  8. There are more exam­ples besides this. But I will stop here. I just want to reit­er­ate that I think we need a real­ist approach to the design of AI.

Participatory AI and ML engineering

In the first half of this year, I’ve pre­sent­ed sev­er­al ver­sions of a brief talk on par­tic­i­pa­to­ry AI. I fig­ured I would post an amal­gam of these to the blog for future ref­er­ence. (Pre­vi­ous­ly, on the blog, I post­ed a brief lit review on the same top­ic; this talk builds on that.)

So, to start, the main point of this talk is that many par­tic­i­pa­to­ry approach­es to AI don’t engage deeply with the specifics of the tech­nol­o­gy. One such spe­cif­ic is the trans­la­tion work engi­neers do to make a prob­lem “learn­able” by a machine (Kang, 2023). From this per­spec­tive, the main ques­tion to ask becomes, how does trans­la­tion hap­pen in our spe­cif­ic projects? Should cit­i­zens be involved in this trans­la­tion work? If so, how to achieve this? 

Before we dig into the state of par­tic­i­pa­to­ry AI, let’s begin by clar­i­fy­ing why we might want to enable par­tic­i­pa­tion in the first place. A com­mon moti­va­tion is a lack of demo­c­ra­t­ic con­trol over AI sys­tems. (This is par­tic­u­lar­ly con­cern­ing when AI sys­tems are used for gov­ern­ment pol­i­cy exe­cu­tion. These are the sys­tems I most­ly look at in my own research.) And so the response is to bring the peo­ple into the devel­op­ment process, and to let them co-decide matters.

In these cas­es, par­tic­i­pa­tion can be under­stood as an enabler of demo­c­ra­t­ic agency, i.e., a way for sub­jects to legit­i­mate the use of AI sys­tems (cf. Peter, 2020 in Rubel et al., 2021). Peter dis­tin­guish­es two path­ways: a nor­ma­tive one and a demo­c­ra­t­ic one. Par­tic­i­pa­tion can be seen as an exam­ple of the demo­c­ra­t­ic path­way to legit­i­ma­tion. A cru­cial detail Peter men­tions here, which is often over­looked in par­tic­i­pa­to­ry AI lit­er­a­ture, is that nor­ma­tive con­straints must lim­it the demo­c­ra­t­ic path­way to avoid arbitrariness.

So, what is the state of par­tic­i­pa­to­ry AI research and prac­tice? I will look at each in turn next.

As men­tioned, I pre­vi­ous­ly post­ed on the state of par­tic­i­pa­to­ry AI research, so I won’t repeat that in full here. (For the record, I reviewed Birhane et al. (2022), Brat­teteig & Verne (2018), Del­ga­do et al. (2023), Ehsan & Riedl (2020), Fef­fer et al. (2023), Gerdes (2022), Groves et al. (2023), Robert­son et al. (2023), Sloane et al. (2020), and Zytko et al. (2022).) Ele­ments that jump out include: 

  • Super­fi­cial and unrep­re­sen­ta­tive involvement.
  • Piece­meal approach­es that have min­i­mal impact on decision-making.
  • Par­tic­i­pants with a con­sul­ta­tive role rather than that of active decision-makers.
  • A lack of bridge-builders between stake­hold­er perspectives.
  • Par­tic­i­pa­tion wash­ing and exploita­tive com­mu­ni­ty involvement.
  • Strug­gles with the dynam­ic nature of tech­nol­o­gy over time.
  • Dis­crep­an­cies between the time scales for users to eval­u­ate design ideas ver­sus the pace at which sys­tems are developed.
  • A demand for par­tic­i­pa­tion to enhance com­mu­ni­ty knowl­edge and to actu­al­ly empow­er them.

Tak­ing a step back, if I were to eval­u­ate the state of the sci­en­tif­ic lit­er­a­ture on par­tic­i­pa­to­ry AI, it strikes me that many of these issues are not new to AI. They have been present in par­tic­i­pa­to­ry design more broad­ly for some time already. Many of these issues are also not nec­es­sar­i­ly spe­cif­ic to AI. The ones I would call out include the issues relat­ed to AI sys­tem dynamism, time scales of par­tic­i­pa­tion ver­sus devel­op­ment, and knowl­edge gaps between var­i­ous actors in par­tic­i­pa­to­ry process­es (and, relat­ed­ly, the lack of bridge-builders).

So, what about prac­tice? Let’s look at two reports that I feel are a good rep­re­sen­ta­tion of the broad­er field: Frame­work for Mean­ing­ful Stake­hold­er Involve­ment by ECNL & Soci­etyIn­side, and Democ­ra­tiz­ing AI: Prin­ci­ples for Mean­ing­ful Pub­lic Par­tic­i­pa­tion by Data & Society.

Frame­work for Mean­ing­ful Stake­hold­er Involve­ment is aimed at busi­ness­es, orga­ni­za­tions, and insti­tu­tions that use AI. It focus­es on human rights, eth­i­cal assess­ment, and com­pli­ance. It aims to be a tool for plan­ning, deliv­er­ing, and eval­u­at­ing stake­hold­er engage­ment effec­tive­ly, empha­siz­ing three core ele­ments: Shared Pur­pose, Trust­wor­thy Process, and Vis­i­ble Impact.

Democ­ra­tiz­ing AI frames pub­lic par­tic­i­pa­tion in AI devel­op­ment as a way to add legit­i­ma­cy and account­abil­i­ty and to help pre­vent harm­ful impacts. It out­lines risks asso­ci­at­ed with AI, includ­ing biased out­comes, opaque deci­sion-mak­ing process­es, and design­ers lack­ing real-world impact aware­ness. Caus­es for inef­fec­tive par­tic­i­pa­tion include uni­di­rec­tion­al com­mu­ni­ca­tion, socioe­co­nom­ic bar­ri­ers, super­fi­cial engage­ment, and inef­fec­tive third-par­ty involve­ment. The report uses envi­ron­men­tal law as a ref­er­ence point and offers eight guide­lines for mean­ing­ful pub­lic par­tic­i­pa­tion in AI.

Tak­ing stock of these reports, we can say that the build­ing blocks for the over­all process are avail­able to those seri­ous­ly look­ing. The chal­lenges fac­ing par­tic­i­pa­to­ry AI are, on the one hand, eco­nom­ic and polit­i­cal. On the oth­er hand, they are relat­ed to the specifics of the tech­nol­o­gy at hand. For the remain­der of this piece, let’s dig into the lat­ter a bit more.

Let’s focus on trans­la­tion work done by engi­neers dur­ing mod­el development.

For this, I build on work by Kang (2023), which focus­es on the qual­i­ta­tive analy­sis of how phe­nom­e­na are trans­lat­ed into ML-com­pat­i­ble forms, pay­ing spe­cif­ic atten­tion to the onto­log­i­cal trans­la­tions that occur in mak­ing a prob­lem learn­able. Trans­la­tion in ML means trans­form­ing com­plex qual­i­ta­tive phe­nom­e­na into quan­tifi­able and com­putable forms. Mul­ti­fac­eted prob­lems are con­vert­ed into a “usable quan­ti­ta­tive ref­er­ence” or “ground truth.” This trans­la­tion is not a mere rep­re­sen­ta­tion of real­i­ty but a refor­mu­la­tion of a prob­lem into math­e­mat­i­cal terms, mak­ing it under­stand­able and process­able by ML algo­rithms. This trans­for­ma­tion involves a sig­nif­i­cant amount of “onto­log­i­cal dis­so­nance,” as it medi­ates and often sim­pli­fies the com­plex­i­ty of real-world phe­nom­e­na into a tax­on­o­my or set of class­es for ML pre­dic­tion. The process of trans­lat­ing is based on assump­tions and stan­dards that may alter the nature of the ML task and intro­duce new social and tech­ni­cal problems. 

So what? I pro­pose we can use the notion of trans­la­tion as a frame for ML engi­neer­ing. Under­stand­ing ML mod­el engi­neer­ing as trans­la­tion is a poten­tial­ly use­ful way to ana­lyze what hap­pens at each step of the process: What gets select­ed for trans­la­tion, how the trans­la­tion is per­formed, and what the result­ing trans­la­tion con­sists of.

So, if we seek to make par­tic­i­pa­to­ry AI engage more with the tech­ni­cal par­tic­u­lar­i­ties of ML, we could begin by iden­ti­fy­ing trans­la­tions that have hap­pened or might hap­pen in our projects. We could then ask to what extent these acts of trans­la­tion are val­ue-laden. For those that are, we could think about how to com­mu­ni­cate these trans­la­tions to a lay audi­ence. A par­tic­u­lar chal­lenge I expect we will be faced with is what the mean­ing­ful lev­el of abstrac­tion for cit­i­zen par­tic­i­pa­tion dur­ing AI devel­op­ment is. We should also ask what the appro­pri­ate ‘vehi­cle’ for cit­i­zen par­tic­i­pa­tion will be. And we should seek to move beyond small-scale, one-off, often unrep­re­sen­ta­tive forms of direct participation.

Bibliography

  • Birhane, A., Isaac, W., Prab­hakaran, V., Diaz, M., Elish, M. C., Gabriel, I., & Mohamed, S. (2022). Pow­er to the Peo­ple? Oppor­tu­ni­ties and Chal­lenges for Par­tic­i­pa­to­ry AI. Equi­ty and Access in Algo­rithms, Mech­a­nisms, and Opti­miza­tion, 1–8. https://doi.org/10/grnj99
  • Brat­teteig, T., & Verne, G. (2018). Does AI make PD obso­lete?: Explor­ing chal­lenges from arti­fi­cial intel­li­gence to par­tic­i­pa­to­ry design. Pro­ceed­ings of the 15th Par­tic­i­pa­to­ry Design Con­fer­ence: Short Papers, Sit­u­at­ed Actions, Work­shops and Tuto­r­i­al — Vol­ume 2, 1–5. https://doi.org/10/ghsn84
  • Del­ga­do, F., Yang, S., Madaio, M., & Yang, Q. (2023). The Par­tic­i­pa­to­ry Turn in AI Design: The­o­ret­i­cal Foun­da­tions and the Cur­rent State of Prac­tice. Pro­ceed­ings of the 3rd ACM Con­fer­ence on Equi­ty and Access in Algo­rithms, Mech­a­nisms, and Opti­miza­tion, 1–23. https://doi.org/10/gs8kvm
  • Ehsan, U., & Riedl, M. O. (2020). Human-Cen­tered Explain­able AI: Towards a Reflec­tive Sociotech­ni­cal Approach. In C. Stephani­dis, M. Kuro­su, H. Degen, & L. Rein­er­man-Jones (Eds.), HCI Inter­na­tion­al 2020—Late Break­ing Papers: Mul­ti­modal­i­ty and Intel­li­gence (pp. 449–466). Springer Inter­na­tion­al Pub­lish­ing. https://doi.org/10/gskmgf
  • Fef­fer, M., Skir­pan, M., Lip­ton, Z., & Hei­dari, H. (2023). From Pref­er­ence Elic­i­ta­tion to Par­tic­i­pa­to­ry ML: A Crit­i­cal Sur­vey & Guide­lines for Future Research. Pro­ceed­ings of the 2023 AAAI/ACM Con­fer­ence on AI, Ethics, and Soci­ety, 38–48. https://doi.org/10/gs8kvx
  • Gerdes, A. (2022). A par­tic­i­pa­to­ry data-cen­tric approach to AI Ethics by Design. Applied Arti­fi­cial Intel­li­gence, 36(1), 2009222. https://doi.org/10/gs8kt4
  • Groves, L., Pep­pin, A., Strait, A., & Bren­nan, J. (2023). Going pub­lic: The role of pub­lic par­tic­i­pa­tion approach­es in com­mer­cial AI labs. Pro­ceed­ings of the 2023 ACM Con­fer­ence on Fair­ness, Account­abil­i­ty, and Trans­paren­cy, 1162–1173. https://doi.org/10/gs8kvs
  • Kang, E. B. (2023). Ground truth trac­ings (GTT): On the epis­temic lim­its of machine learn­ing. Big Data & Soci­ety, 10(1), 1–12. https://doi.org/10/gtfgvx
  • Peter, F. (2020). The Grounds of Polit­i­cal Legit­i­ma­cy. Jour­nal of the Amer­i­can Philo­soph­i­cal Asso­ci­a­tion, 6(3), 372–390. https://doi.org/10/grqfhn
  • Robert­son, S., Nguyen, T., Hu, C., Albis­ton, C., Nikzad, A., & Sale­hi, N. (2023). Expres­sive­ness, Cost, and Col­lec­tivism: How the Design of Pref­er­ence Lan­guages Shapes Par­tic­i­pa­tion in Algo­rith­mic Deci­sion-Mak­ing. Pro­ceed­ings of the 2023 CHI Con­fer­ence on Human Fac­tors in Com­put­ing Sys­tems, 1–16. https://doi.org/10/gr6q2t
  • Rubel, A., Cas­tro, C., & Pham, A. K. (2021). Algo­rithms and auton­o­my: The ethics of auto­mat­ed deci­sion sys­tems. Cam­bridge Uni­ver­si­ty Press.
  • Sloane, M., Moss, E., Awom­o­lo, O., & For­lano, L. (2020). Par­tic­i­pa­tion is not a Design Fix for Machine Learn­ing. arXiv:2007.02423 [Cs]. http://arxiv.org/abs/2007.02423
  • Zytko, D., J. Wis­niews­ki, P., Guha, S., P. S. Baumer, E., & Lee, M. K. (2022). Par­tic­i­pa­to­ry Design of AI Sys­tems: Oppor­tu­ni­ties and Chal­lenges Across Diverse Users, Rela­tion­ships, and Appli­ca­tion Domains. Extend­ed Abstracts of the 2022 CHI Con­fer­ence on Human Fac­tors in Com­put­ing Sys­tems, 1–4. https://doi.org/10/gs8kv6

PhD update – June 2024

I am writ­ing this final PhD update as a fresh­ly mint­ed doc­tor. On Thurs­day, May 23, 2024, I suc­cess­ful­ly defend­ed my the­sis, ‘Con­testable Arti­fi­cial Intel­li­gence: Con­struc­tive Design Research for Pub­lic Arti­fi­cial Intel­li­gence Sys­tems that are Open and Respon­sive to Dispute.’

I start­ed the PhD on Sep­tem­ber 1, 2018 (read the very first update post­ed on that day here). So, that’s five years, eight months, 23 days from start to fin­ish. It has been quite the jour­ney, and I feel hap­py and relieved to have com­plet­ed it. I am proud of the work embod­ied in the the­sis. Most of all, I am thank­ful for the trans­for­ma­tive learn­ing expe­ri­ence, none of which would have been pos­si­ble with­out the sup­port of my super­vi­sors Gerd, Ianus, and Neelke.

On the day itself, I was hon­ored to have as my exter­nal com­mit­tee mem­bers pro­fes­sors Dignum, Löw­gren, van Zoo­nen, and van de Poel, pro­fes­sor Voûte as the chair, and Joost and Mireia as my paranymphs.

The the­sis PDF can be down­loaded at the TU Delft repos­i­to­ry, and a video of the pro­ceed­ings is avail­able on YouTube.

Me, with a copy of the the­sis, short­ly before start­ing the layper­son­’s talk. Pho­to: Roy Borgh­outs.

Recent events

Review­ing my notes since the last update, below are some more notable things that hap­pened in the past eight months.

  • I ran a short work­shop on AI Ped­a­gogy Through A Design Lens, togeth­er with Hosana Morales, at the TU Delft spring sym­po­sium on AI edu­ca­tion. Read the post.
  • A sto­ry about my research was pub­lished on the TU Delft indus­tri­al design engi­neer­ing web­site in the run-up to my defense on May 14, 2024. Read the sto­ry.
  • I updat­ed and ran the fifth and final iter­a­tion of the AI & Soci­ety indus­tri­al design engi­neer­ing mas­ter elec­tive course from Feb­ru­ary 28 through April 10, 2024. A pre­vi­ous ver­sion is doc­u­ment­ed here, which I plan to update some­time in the near future.
  • I gave a talk titled Con­testable AI: Design­ing for Human Auton­o­my at the Ams­ter­dam UX meet­up on Feb­ru­ary 21, 2024. Down­load the slides.
  • The out­comes of a design sprint on tools for third-par­ty scruti­ny, orga­nized by the Respon­si­ble Sens­ing Lab, which took inspi­ra­tion from my research, were pub­lished on Decem­ber 7, 2023. Read the report.
  • I was inter­viewed by Mireia Yur­ri­ta Sem­per­e­na for a DCODE pod­cast episode titled Beyond Val­ues in Algo­rith­mic Design, pub­lished Novem­ber 6, 2023. Lis­ten to the episode.
  • Togeth­er with Clau­dio Sar­ra and Mar­co Alma­da, I host­ed an online sem­i­nar titled Build­ing Con­testable Sys­tems on Octo­ber 26, 2023. Read the thread.
  • I was a pan­elist at the Design & AI Sym­po­sium 2023 on Octo­ber 18, 2023.
  • A paper I co-authored titled When ‘Doing Ethics’ Meets Pub­lic Pro­cure­ment of Smart City Tech­nol­o­gy – an Ams­ter­dam Case Study, was pre­sent­ed by first author Mike de Kreek at IASDR 2023 on Octo­ber 9–13. Read the paper.

Looking ahead

I will con­tin­ue at TU Delft as a post­doc­tor­al researcher and will stay focused on design, AI, and pol­i­tics, but I will try to evolve my research into some­thing that builds on my the­sis work but adds a new angle.

The Envi­sion­ing Con­testa­bil­i­ty Loops arti­cle men­tioned in pre­vi­ous updates is now in press with She Ji, which I am very pleased about. It should be pub­lished “soon.”

Upcom­ing appear­ances include a brief talk on par­tic­i­pa­to­ry AI at a Cities Coali­tion for Dig­i­tal Rights event and a pre­sen­ta­tion as part of a pan­el on The Mutu­al Shap­ing Of Demo­c­ra­t­ic Prac­tices And AI at TILT­ing Per­spec­tives 2024.

That’s it for this final PhD update. I will prob­a­bly con­tin­ue these posts under a new title. We’ll see.

Participatory AI literature review

I’ve been think­ing alot about civic par­tic­i­pa­tion in machine learn­ing sys­tems devel­op­ment. In par­tic­u­lar, involv­ing non-experts in the poten­tial­ly val­ue-laden trans­la­tion work from spec­i­fi­ca­tions that engi­neers do when they build their mod­els. Below is a sum­ma­ry of a selec­tion of lit­er­a­ture I found on the top­ic, which may serve as a jump­ing-off point for future research.

Abstract

The lit­er­a­ture on par­tic­i­pa­to­ry arti­fi­cial intel­li­gence (AI) reveals a com­plex land­scape marked by chal­lenges and evolv­ing method­olo­gies. Fef­fer et al. (2023) cri­tique the reduc­tion of par­tic­i­pa­tion to com­pu­ta­tion­al mech­a­nisms that only approx­i­mate nar­row moral val­ues. They also note that engage­ments with stake­hold­ers are often super­fi­cial and unrep­re­sen­ta­tive. Groves et al. (2023) iden­ti­fy sig­nif­i­cant bar­ri­ers in com­mer­cial AI labs, includ­ing high costs, frag­ment­ed approach­es, exploita­tion con­cerns, lack of trans­paren­cy, and con­tex­tu­al com­plex­i­ties. These bar­ri­ers lead to a piece­meal approach to par­tic­i­pa­tion with min­i­mal impact on deci­sion-mak­ing in AI labs. Del­ga­do et al. (2023) observe that par­tic­i­pa­to­ry AI involves stake­hold­ers most­ly in a con­sul­ta­tive role with­out inte­grat­ing them as active deci­sion-mak­ers through­out the AI design lifecycle.

Gerdes (2022) pro­pos­es a data-cen­tric approach to AI ethics and under­scores the need for inter­dis­ci­pli­nary bridge builders to rec­on­cile dif­fer­ent stake­hold­er per­spec­tives. Robert­son et al. (2023) explore par­tic­i­pa­to­ry algo­rithm design, empha­siz­ing the need for pref­er­ence lan­guages that bal­ance expres­sive­ness, cost, and collectivism—Sloane et al. (2020) cau­tion against “par­tic­i­pa­tion wash­ing” and the poten­tial for exploita­tive com­mu­ni­ty involve­ment. Brat­teteig & Verne (2018) high­light AI’s chal­lenges to tra­di­tion­al par­tic­i­pa­to­ry design (PD) meth­ods, includ­ing unpre­dictable tech­no­log­i­cal changes and a lack of user-ori­ent­ed eval­u­a­tion. Birhane et al. (2022) call for a clear­er under­stand­ing of mean­ing­ful par­tic­i­pa­tion, advo­cat­ing for a shift towards vibrant, con­tin­u­ous engage­ment that enhances com­mu­ni­ty knowl­edge and empow­er­ment. The lit­er­a­ture sug­gests a press­ing need for more effec­tive, inclu­sive, and empow­er­ing par­tic­i­pa­to­ry approach­es in AI development.

Bibliography

  1. Birhane, A., Isaac, W., Prab­hakaran, V., Diaz, M., Elish, M. C., Gabriel, I., & Mohamed, S. (2022). Pow­er to the Peo­ple? Oppor­tu­ni­ties and Chal­lenges for Par­tic­i­pa­to­ry AI. Equi­ty and Access in Algo­rithms, Mech­a­nisms, and Opti­miza­tion, 1–8. https://doi.org/10/grnj99
  2. Brat­teteig, T., & Verne, G. (2018). Does AI make PD obso­lete?: Explor­ing chal­lenges from arti­fi­cial intel­li­gence to par­tic­i­pa­to­ry design. Pro­ceed­ings of the 15th Par­tic­i­pa­to­ry Design Con­fer­ence: Short Papers, Sit­u­at­ed Actions, Work­shops and Tuto­r­i­al — Vol­ume 2, 1–5. https://doi.org/10/ghsn84
  3. Del­ga­do, F., Yang, S., Madaio, M., & Yang, Q. (2023). The Par­tic­i­pa­to­ry Turn in AI Design: The­o­ret­i­cal Foun­da­tions and the Cur­rent State of Prac­tice. Pro­ceed­ings of the 3rd ACM Con­fer­ence on Equi­ty and Access in Algo­rithms, Mech­a­nisms, and Opti­miza­tion, 1–23. https://doi.org/10/gs8kvm
  4. Ehsan, U., & Riedl, M. O. (2020). Human-Cen­tered Explain­able AI: Towards a Reflec­tive Sociotech­ni­cal Approach. In C. Stephani­dis, M. Kuro­su, H. Degen, & L. Rein­er­man-Jones (Eds.), HCI Inter­na­tion­al 2020—Late Break­ing Papers: Mul­ti­modal­i­ty and Intel­li­gence (pp. 449–466). Springer Inter­na­tion­al Pub­lish­ing. https://doi.org/10/gskmgf
  5. Fef­fer, M., Skir­pan, M., Lip­ton, Z., & Hei­dari, H. (2023). From Pref­er­ence Elic­i­ta­tion to Par­tic­i­pa­to­ry ML: A Crit­i­cal Sur­vey & Guide­lines for Future Research. Pro­ceed­ings of the 2023 AAAI/ACM Con­fer­ence on AI, Ethics, and Soci­ety, 38–48. https://doi.org/10/gs8kvx
  6. Gerdes, A. (2022). A par­tic­i­pa­to­ry data-cen­tric approach to AI Ethics by Design. Applied Arti­fi­cial Intel­li­gence, 36(1), 2009222. https://doi.org/10/gs8kt4
  7. Groves, L., Pep­pin, A., Strait, A., & Bren­nan, J. (2023). Going pub­lic: The role of pub­lic par­tic­i­pa­tion approach­es in com­mer­cial AI labs. Pro­ceed­ings of the 2023 ACM Con­fer­ence on Fair­ness, Account­abil­i­ty, and Trans­paren­cy, 1162–1173. https://doi.org/10/gs8kvs
  8. Robert­son, S., Nguyen, T., Hu, C., Albis­ton, C., Nikzad, A., & Sale­hi, N. (2023). Expres­sive­ness, Cost, and Col­lec­tivism: How the Design of Pref­er­ence Lan­guages Shapes Par­tic­i­pa­tion in Algo­rith­mic Deci­sion-Mak­ing. Pro­ceed­ings of the 2023 CHI Con­fer­ence on Human Fac­tors in Com­put­ing Sys­tems, 1–16. https://doi.org/10/gr6q2t
  9. Sloane, M., Moss, E., Awom­o­lo, O., & For­lano, L. (2020). Par­tic­i­pa­tion is not a Design Fix for Machine Learn­ing. arXiv:2007.02423 [Cs]. http://arxiv.org/abs/2007.02423
  10. Zytko, D., J. Wis­niews­ki, P., Guha, S., P. S. Baumer, E., & Lee, M. K. (2022). Par­tic­i­pa­to­ry Design of AI Sys­tems: Oppor­tu­ni­ties and Chal­lenges Across Diverse Users, Rela­tion­ships, and Appli­ca­tion Domains. Extend­ed Abstracts of the 2022 CHI Con­fer­ence on Human Fac­tors in Com­put­ing Sys­tems, 1–4. https://doi.org/10/gs8kv6

PhD update – September 2023

I’m back again with anoth­er Ph.D. update. Five years after I start­ed in Delft, we are near­ing the fin­ish line on this whole thing. But before we look ahead, let’s review notable events since the pre­vi­ous update in March 2023.

Occurrences

  1. I pre­sent­ed our frame­work, Con­testable AI by Design, at the annu­al NWO ICT Open con­fer­ence, which, for the first time, had an entire track ded­i­cat­ed to HCI research in the Nether­lands. It was an excel­lent oppor­tu­ni­ty to meet fel­low researchers from oth­er Dutch insti­tu­tions. The slides are avail­able as PDF at contestable.ai.
  2. I vis­it­ed Ham­burg to present our paper, Con­testable Cam­era Cars, at CHI 2023. We also received a Best Paper award, which I am, of course, very pleased with. The con­fer­ence was equal parts inspir­ing and over­whelm­ing. The best part of it was meet­ing in-per­son researchers who shared my interests.
  3. Also, at CHI, I was inter­viewed about my research by Mike Green for his pod­cast Under­stand­ing Users. You can lis­ten to it here. It is always good prac­tice to try and lay out some of my argu­ments spon­ta­neous­ly live.
  4. In June, I joined a pan­el at a BOLD Cities “talk show” to dis­cuss the design of smart city sys­tems for con­testa­bil­i­ty. It was quite an hon­or to be on the same pan­el as Eef­je Cup­pen, direc­tor of the Rathenau Insti­tute. This event was great because we had tech­no­log­i­cal, design, polit­i­cal, and pol­i­cy per­spec­tives. Sev­er­al guests argued for the need to rein­vig­o­rate rep­re­sen­ta­tive democ­ra­cy and give a more promi­nent role to elect­ed politi­cians in set­ting tech­nol­o­gy pol­i­cy. A report is avail­able here.
  5. In August, the BRIDE project had its clos­ing event. This is the NWO research project that par­tial­ly fund­ed my Ph.D. The event was an excel­lent oppor­tu­ni­ty to reflect on our work togeth­er over the past years. I took the oppor­tu­ni­ty to revis­it the work of Sask­ia Sassen on city­ness and to think through some of the impli­ca­tions of my work on con­testa­bil­i­ty for the field of smart urban­ism. The slides are avail­able at contestable.ai.
  6. Final­ly, last week, a short opin­ion piece that lays out the argu­ment for con­testable AI in what I hope is a rea­son­ably acces­si­ble man­ner, was pub­lished on the TU Delft website.
Photo of Eefje Cuppen and I being interviewed by Inge Janse at the BOLD Cities talk show on June 22, 2023—photo by Tiffany Konings.

Eef­je Cup­pen and I being inter­viewed by Inge Janse at the BOLD Cities talk show on June 22, 2023—photo by Tiffany Konings.

Envisioning Contestability Loops

Through­out this, I have been dili­gent­ly chip­ping away at my final pub­li­ca­tion, “Envi­sion­ing Con­testa­bil­i­ty Loops: Eval­u­at­ing the Ago­nis­tic Are­na as a Gen­er­a­tive Metaphor for Pub­lic AI.” I had a great time col­lab­o­rat­ing with Leon de Korte on an info­graph­ic of part of my design framework.

We took this info­graph­ic on a tour of Dutch inter­ac­tion design agen­cies and con­duct­ed con­cept design work­shops. I enjoyed return­ing to prac­tice and shar­ing the work of the past cou­ple of years with peers in prac­tice. My friends at Eend wrote a nice blog post about it.

The analy­sis of the out­comes of these work­shops forms the basis for the arti­cle, in which I explore the degree to which the guid­ing con­cept (gen­er­a­tive metaphor) behind con­testable AI, which I have dubbed the “Ago­nis­tic Are­na” is a pro­duc­tive one for design prac­ti­tion­ers. Spoil­ers: It is, but com­pet­ing metaphors are also at play in the pub­lic AI design space.

The man­u­script is close to com­ple­tion. As usu­al, putting some­thing like this togeth­er is a heavy but grat­i­fy­ing lift. I look for­ward to shar­ing the results and the under­ly­ing info­graph­ic with the broad­er world.

Are we there yet?

Look­ing ahead, I will be on a pan­el along­side the great Julian Bleeck­er and a host of oth­ers at the annu­al TU Delft Design & AI sym­po­sium in October.

But aside from that, I will keep my head down and focus on com­plet­ing my the­sis. The aim is to hand it in by the end of Novem­ber. So, two more months on the clock. Will I make it? Let’s find out!

PhD update – March 2023

Hel­lo again, and wel­come to anoth­er update on my Ph.D. research progress. I will briefly run down the things that hap­pened since the last update, what I am cur­rent­ly work­ing on, and some notable events on the horizon.

Recent happenings

CHI 2023 paper

Stills from Con­testable Cam­era Cars con­cept video.

First off, the big news is that the paper I sub­mit­ted to CHI 2023 was accept­ed. This is a big deal for me because HCI is the core field I aim to con­tribute to, and CHI is its flag­ship conference.

Here’s the full citation:

Alfrink, K., Keller, I., Doorn, N., & Kortuem, G. (2023). Con­testable Cam­era Cars: A Spec­u­la­tive Design Explo­ration of Pub­lic AI That Is Open and Respon­sive to Dis­pute. https://doi.org/10/jwrx

I have had sev­er­al papers reject­ed in the past (CHI is noto­ri­ous­ly hard to get accept­ed at), so I feel vin­di­cat­ed. The paper is already avail­able as an arX­iv preprint, as is the con­cept video that forms the core of the study I report on (many thanks to my pal Simon for col­lab­o­rat­ing on this with me). CHI 2023 hap­pens in late April. I will be rid­ing a train over there to present the paper in per­son. Very much look­ing for­ward to that.

Con­testable Cam­era Cars con­cept video.

Responsible Sensing Lab anniversary event

I briefly pre­sent­ed my research at the Respon­si­ble Sens­ing Lab anniver­sary event on Feb­ru­ary 16. The whole event was quite enjoy­able, and I got some encour­ag­ing respons­es to my ideas after­ward which is always nice. The event was record­ed in full. My appear­ance starts around the 1:47:00 mark.

It me. (Cred­it: Respon­si­ble Sens­ing Lab.)
Video of my con­tri­bu­tion. (Pakhuis de Zwi­jger / Respon­si­ble Sens­ing Lab.)

Tweeting, tooting, blogging

I have been get­ting back into the habit of tweet­ing, toot­ing, and even the occa­sion­al spot of blog­ging on this web­site again. As the end of my Ph.D. nears, I fig­ured it might be worth it to engage more active­ly with “the dis­course,” as they say. I most­ly share stuff I read that is relat­ed to my research and that I find inter­est­ing. Although, of course, posts relat­ed to my twin sons’ music taste and strug­gles with uni­ver­si­ty bureau­cra­cy always win out in the end. (Yes, I am aware my tim­ing is ter­ri­ble, see­ing as how we have basi­cal­ly final­ly con­clud­ed social media was a bad idea after all.)

Current activities

Envisioning Contestability Loops

At the moment, the major­i­ty of my time is tak­en up by con­duct­ing a final study (work­ing title: “Envi­sion­ing Con­testa­bil­i­ty Loops”). I am excit­ed about this one because I get to once again col­lab­o­rate with a pro­fes­sion­al design­er on an arti­fact, in this case, a visu­al expla­na­tion of my frame­work, and use the result as a research instru­ment to dig into, in this case, the strengths and weak­ness­es of con­testa­bil­i­ty as a gen­er­a­tive metaphor for the design of pub­lic AI.

Thesis

In par­al­lel, I have begun to put togeth­er my the­sis. It is paper-based, but of course, the intro­duc­to­ry and con­clud­ing chap­ters require some thought still.

The aim is to have both the final arti­cle and the­sis fin­ished by the end of sum­mer and then begin the ardu­ous process of get­ting a date for my defense, assem­bling a com­mit­tee, etc.

Agonistic Machine Vision Development

In the mean­time, I am also men­tor­ing Lau­ra, anoth­er bril­liant mas­ter grad­u­a­tion stu­dent. Her project, titled “Ago­nis­tic Machine Vision Devel­op­ment,” builds on my pre­vi­ous research. In par­tic­u­lar, one of the chal­lenges I iden­ti­fied in Con­testable Cam­era Cars, that of the dif­fer­en­tial in infor­ma­tion posi­tion between cit­i­zens and experts when they col­lab­o­rate in par­tic­i­pa­to­ry machine learn­ing ses­sions. It’s very grat­i­fy­ing to see oth­ers do design work that push­es these ideas further.

Upcoming events

So yeah, like I already men­tioned, I will be speak­ing at CHI 2023, which takes place on 23–28 April in Ham­burg. The sched­ule says I am pre­sent­ing on April 25 as part of the ses­sion on “AI Trust, Trans­paren­cy and Fair­ness”, which includes some excel­lent-look­ing contributions.

And before that, I will be at ICT.OPEN in Utrecht on April 20 to present briefly on the Con­testable AI by Design frame­work as part of the CHI NL track. It should be fun.

…

That’s it for this update. Maybe, by the time the next one rolls around, I will be able to share a date for my defense. But let’s not jinx it.

Tensions in the professional field of design

I liked a pas­sage in a Kees Dorst paper on “aca­d­e­m­ic design” so much, I turned it into a lit­tle diagram.

Ten­sions in the pro­fes­sion­al field of design. (PDF)

Note that these ten­sions are inde­pen­dent of each oth­er. The dia­gram does not imply two “sides” of design. At any giv­en moment, a design activ­i­ty can be plot­ted on each axis inde­pen­dent­ly. This is also not an exhaus­tive list of ten­sions. Final­ly, Dorst claims these ten­sions are irreconcilable.

The orig­i­nal passage:

Con­tem­po­rary devel­op­ments in design can be described and under­stood in much the same way. The pro­fes­sion­al field that we so eas­i­ly label ‘design’ is com­plex, and full of inner con­tra­dic­tions. These inner ten­sions feed the dis­cus­sions in the field. To name a few: (1) the objec­tives of design and the moti­va­tion of design­ers can range from com­mer­cial suc­cess to the com­mon good. (2) The role and posi­tion of the design­er can be as an autonomous cre­ator, or as a prob­lem solver in-ser­vice to the client. (3) The dri­ve of the design­er can be ide­al­is­tic, or it can be more prag­mat­ic (4) The result­ing design can be a ‘thing’, but also imma­te­r­i­al (5) The basis for the process of design­ing can be intu­itive, or based on knowl­edge and research… Etcetera… The devel­op­ment of the design dis­ci­plines can be traced along these lines of ten­sion — with design­ers in dif­fer­ent envi­ron­ments and times chang­ing posi­tion rel­a­tive to these fun­da­men­tal para­dox­es, but nev­er resolv­ing them. Ulti­mate­ly, the real strength and coher­ence of design as a field of pro­fes­sions comes from rec­og­niz­ing these con­tra­dic­tions, and the dynam­ics of the field is a result of con­tin­u­ous exper­i­men­ta­tion along the rifts defined by them. Rather than a com­mon set of prac­tices and skills that design­ers might have [Cross, 1990] it is these inner con­tra­dic­tions in design that define its cul­ture, its men­tal­i­ty. Design research should be an active force in these dis­cus­sions, build­ing bridges between them where pos­si­ble. Not to resolve them into a mono­lith­ic Sci­ence of Design, but advanc­ing the dis­cus­sion in this dynam­i­cal­ly shift­ing set of relations.

Dorst, K. (2016, June 27). Design prac­tice and design research: Final­ly togeth­er? Pro­ceed­ings of DRS 2016. Design Research Soci­ety 50th Anniver­sary Con­fer­ence, Brighton, UK. https://www.drs2016.org/212

Citizen participation in “The End of the End of History”

Below are some choice quotes on “cit­i­zen par­tic­i­pa­tion” from chap­ter 8 of The End of the End of His­to­ry, a rec­om­mend­ed book on our recent glob­al polit­i­cal his­to­ry. I feel like many of us in the par­tic­i­pa­to­ry tech­nol­o­gy design space are com­plic­it in these prac­tices to some extent. I con­tin­ue to grap­ple with alter­na­tive mod­els of mass demo­c­ra­t­ic con­trol over technology.

The Cen­ter-Left will pro­pose a range of mea­sures designed to pro­mote “civic engage­ment” or “com­mu­ni­ty participation.”

Cit­i­zens’ sum­mits, juries and pan­els all aim at par­tic­i­pa­tion rather than pow­er, at the tech­no­crat­ic incor­po­ra­tion of the peo­ple into pol­i­tics in order to man­age away conflict.

Like­wise the pop­u­lar­i­ty of delib­er­a­tive modes of engage­ment, delib­er­a­tive stake­hold­er events or work­shops are char­ac­ter­is­tic tools of tech­no­crat­ic do-good­ers as they cre­ate the sim­u­lacrum of a demo­c­ra­t­ic process in which peo­ple are assem­bled to pro­vide an osten­si­bly col­lec­tive solu­tion to a prob­lem, but deci­sions lack a bind­ing qual­i­ty or have already been tak­en in advance.

Though unable to gain trac­tion at a transna­tion­al lev­el, the Left may find some suc­cess in munic­i­pal pol­i­tics, fol­low­ing the 2010s exam­ple of Barcelona.

Side­step­ping […] ani­mus toward Big Tech com­pa­nies, [tech solu­tion­ism (Moro­zov, 2013) and the ide­ol­o­gy of ease (Green­field, 2017)] may come to be applied to non-mar­ket activ­i­ties, such as solv­ing com­mu­ni­ty prob­lems, per­haps at the lev­el of munic­i­pal government.

Sov­er­eign, nation­al pol­i­tics – which neolib­er­al­ism was designed to defang – will remain beyond the grasp of the Left. Pro­gres­sives will pre­fer instead to oper­ate at the munic­i­pal, the every­day or the supra­na­tion­al lev­el – pre­cise­ly the are­na to which neolib­er­al­ism sought to dis­place pol­i­tics, to where it could do no harm.

Hochuli, A., Hoare, G., & Cun­liffe, P. (2021). The End of the End of His­to­ry. Zero Books.

De opkomst van de meritocratie

Thi­js Klein­paste heeft een mooie boekbe­sprek­ing van Michael Young’s De opkomst van de mer­i­to­cratie in de Ned­er­landse Boekengids. Een paar pas­sages die ik vooral sterk vond hieronder.

De grote ver­di­en­ste van Young is dat hij inzichtelijk maakt hoe onschuldige principes als ‘beloning naar ver­di­en­ste’ volkomen kun­nen ontsporen als ze wor­den ingezet bin­nen een verder onveran­derd soci­aal en economisch stelsel. Con­creet: som­mi­gen een uitverko­ren posi­tie geven in een maatschap­pelijke hiërar­chie en anderen opdra­gen om hun plek te kennen. 

Het klassen­be­lang van de mer­i­to­cratie is abstracter. Het belan­grijk­ste is om allereerst een klasse of kaste te bli­jven om zo de voorde­len daar­van te kun­nen bli­jven oog­sten. In iedere mod­erne staat wordt macht uit­geoe­fend – of mer­i­to­cratis­ch­er gezegd: moet er bestu­urd wor­den – en als er dan toch een kaste moet zijn die deze taak vervult, laat dat die van de hoogst gediplomeer­den zijn. De mer­i­to­cratie repro­duceert zichzelf door deze gedachte mee te geven aan elke nieuwe licht­ing die tot haar uitverko­ren rangen toe­treedt: dat zij de juiste, met recht geroepen groep is om de wereld te orde­nen. Niet de arbei­der­sklasse, niet de ongelei­de democ­ra­tie, niet het gekri­oel van belan­gen­groep­jes – maar zij. Alle mater­iële voorde­len van de mer­i­to­cratie vloeien voort uit het in stand houden van die uitverko­ren status.

Te vaak lijkt de gedachte te zijn dat verte­gen­wo­ordig­ing en het bedi­enen van belan­gen onprob­lema­tisch in elka­ars ver­lengde liggen. Om die zelfge­noegza­amheid te door­breken is ken­nelijk iets stel­ligers nodig, zoals de gedachte dat waar man­agers en bestu­ur­ders zijn, er ges­taakt moet kun­nen wor­den: dat waar macht wordt uit­geoe­fend en waar aan­wi­jzin­gen wor­den gegeven, zij die de aan­wi­jzin­gen moeten opvol­gen kun­nen stem­men met hun voeten. Dat con­flict omar­md wordt en niet wordt gezien als iets wat gevaar­lijk is voor de maatschap­pelijke lieve vrede, de ‘economie’, of zelfs de democ­ra­tie. Con­flict is ongetwi­jfeld gevaar­lijk voor de hege­monie van de man­ag­er en diens klasse van droomkoninkjes, en daarmee voor de soev­ere­initeit van de mer­i­to­cratis­che orde, maar dat gevaar is zow­el heilza­am als noodza­ke­lijk. Een van de lessen van het boek van Young is immers ook dat je moet kiezen: zelf een rev­o­lu­tie mak­en, of wacht­en tot die uitbreekt.

Zelf lezen: https://www.nederlandseboekengids.com/20221116-thijs-kleinpaste/

PhD update – September 2022

Sev­en months since the last update. Much bet­ter than the gap of three years between the pre­vi­ous two. These past months I feel like I have begun to reap the rewards of the grunt work of the last cou­ple of years. Two papers final­ly saw the light of day, as well as a course syl­labus. Read on for some more details.

Things that hap­pened:

First, a pair of talks. In Feb­ru­ary I pre­sent­ed on “Con­testable AI & Civic Co-Design” as part of a pan­el chaired by Roy Ben­dor at Rein­vent­ing the City. A PDF of my slides is avail­able on the contestable.ai web­site, here. In March, I pre­sent­ed at the AiTech Ago­ra. The title of the talk is “Mean­ing­ful Human Con­trol Through Con­testa­bil­i­ty by Design” and the slides are avail­able here.

In Feb­ru­ary a short inter­view was pub­lished by Bold Cities, a smart city research cen­ter I am loose­ly affil­i­at­ed with.

Then, in March, came a big moment for me, with the pub­li­ca­tion of my first jour­nal arti­cle in AI & Soci­ety. Here’s the abstract, and ref­er­ence. It’s avail­able open access.

The increas­ing use of arti­fi­cial intel­li­gence (AI) by pub­lic actors has led to a push for more trans­paren­cy. Pre­vi­ous research has con­cep­tu­al­ized AI trans­paren­cy as knowl­edge that empow­ers cit­i­zens and experts to make informed choic­es about the use and gov­er­nance of AI. Con­verse­ly, in this paper, we crit­i­cal­ly exam­ine if trans­paren­cy-as-knowl­edge is an appro­pri­ate con­cept for a pub­lic realm where pri­vate inter­ests inter­sect with demo­c­ra­t­ic con­cerns. We con­duct a prac­tice-based design research study in which we pro­to­type and eval­u­ate a trans­par­ent smart elec­tric vehi­cle charge point, and inves­ti­gate experts’ and cit­i­zens’ under­stand­ing of AI trans­paren­cy. We find that cit­i­zens expe­ri­ence trans­paren­cy as bur­den­some; experts hope trans­paren­cy ensures accep­tance, while cit­i­zens are most­ly indif­fer­ent to AI; and with absent means of con­trol, cit­i­zens ques­tion transparency’s rel­e­vance. The ten­sions we iden­ti­fy sug­gest trans­paren­cy can­not be reduced to a prod­uct fea­ture, but should be seen as a medi­a­tor of debate between experts and citizens.

Alfrink, Kars, Ianus Keller, Neelke Doorn, and Gerd Kortuem. “Ten­sions in Trans­par­ent Urban AI: Design­ing a Smart Elec­tric Vehi­cle Charge Point.” AI & SOCIETY, March 31, 2022. https://doi.org/10/gpszwh.

In April, the Respon­si­ble Sens­ing Lab pub­lished a report on “Respon­si­ble Drones”, to which I con­tributed a lit­tle as par­tic­i­pant on work­shops that lead up to it.

A sec­ond big mile­stone for me was mak­ing pub­lic the syl­labus for indus­tri­al design engi­neer­ing mas­ter elec­tive course “AI & Soci­ety” (no rela­tion to the jour­nal) which I have been devel­op­ing under the guid­ance of my super­vi­sor Gerd Kortuem over the past cou­ple of years. The syl­labus con­tains a read­ing list, as well as many self-guid­ed design exer­cis­es. Here’s a short description:

Arti­fi­cial Intel­li­gence (AI) is increas­ing­ly used by a vari­ety of orga­ni­za­tions in ways that impact soci­ety at scale. This 6 EC mas­ter elec­tive course aims to equip stu­dents with tools and meth­ods for the respon­si­ble design of pub­lic AI. Dur­ing sev­en weeks stu­dents attend a full-day ses­sion of lec­tures and work­shops. Stu­dents col­lab­o­rate on a group design project through­out. At the end, stu­dents indi­vid­u­al­ly deliv­er a short paper.

ID5417 Arti­fi­cial Intel­li­gence and Society

The third big mile­stone was the pub­li­ca­tion of my sec­ond jour­nal arti­cle in Minds & Machines. It is the the­o­ret­i­cal cor­ner­stone of my the­sis, a pro­vi­sion­al frame­work for design­ing con­testa­bil­i­ty into AI sys­tems. Abstract and ref­er­ence fol­low. This one is also open access.

As the use of AI sys­tems con­tin­ues to increase, so do con­cerns over their lack of fair­ness, legit­i­ma­cy and account­abil­i­ty. Such harm­ful auto­mat­ed deci­sion-mak­ing can be guard­ed against by ensur­ing AI sys­tems are con­testable by design: respon­sive to human inter­ven­tion through­out the sys­tem life­cy­cle. Con­testable AI by design is a small but grow­ing field of research. How­ev­er, most avail­able knowl­edge requires a sig­nif­i­cant amount of trans­la­tion to be applic­a­ble in prac­tice. A proven way of con­vey­ing inter­me­di­ate-lev­el, gen­er­a­tive design knowl­edge is in the form of frame­works. In this arti­cle we use qual­i­ta­tive-inter­pre­ta­tive meth­ods and visu­al map­ping tech­niques to extract from the lit­er­a­ture sociotech­ni­cal fea­tures and prac­tices that con­tribute to con­testable AI, and syn­the­size these into a design framework. 

Alfrink, Kars, Ianus Keller, Gerd Kortuem, and Neelke Doorn. “Con­testable AI by Design: Towards a Frame­work.” Minds and Machines, August 13, 2022. https://doi.org/10/gqnjcs.

Around the same time in August, Fabi­an Geis­er, whom I had been men­tor­ing for some time, grad­u­at­ed with a fas­ci­nat­ing mas­ter the­sis and project with the title “Reimag­in­ing the smart allo­ca­tion of road space in Ams­ter­dam for fair­ness”.

And final­ly, as these things were going on, I have been qui­et­ly chip­ping away at a third paper that applies the con­testable AI by design frame­work to the phe­nom­e­non of cam­era cars used by munic­i­pal­i­ties. My aim was to cre­ate an exam­ple of what I mean by con­testable AI, and use the exam­ple to inter­view civ­il ser­vants about their views on the chal­lenges fac­ing imple­men­ta­tion of con­testa­bil­i­ty in the pub­lic AI sys­tems they are involved with. I’ve sub­mit­ted the man­u­script, titled “Con­testable Cam­era Cars: A spec­u­la­tive design explo­ration of pub­lic AI that is open and respon­sive to dis­pute”, to CHI, and will hear back ear­ly Novem­ber. Fin­gers crossed for that one.

Look­ing ahead:

So what’s next? Well, I have lit­tle under a year left on my PhD con­tract, so I should real­ly begin wrap­ping up. I am con­sid­er­ing a final pub­li­ca­tion, but have not set­tled on any top­ic in par­tic­u­lar yet. Cur­rent inter­ests include AI sys­tem mon­i­tor­ing, visu­al meth­ods, and more besides. Once that final paper is in the can I will turn my atten­tion to putting togeth­er the the­sis itself, which is paper-based, so most­ly requires writ­ing an over­all intro­duc­tion and con­clu­sion to book­end the includ­ed pub­li­ca­tions. Should be a piece of cake, right?

And after the PhD? I am not sure yet, but I hope to remain involved in research and teach­ing, while at the same time per­haps get­ting a bit more back into design prac­tice besides. If at all pos­si­ble, hope­ful­ly in the domain of pub­lic sec­tor appli­ca­tions of AI.

That’s it for this update. I will be back at some point when there is more news to share.