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 – January 2022

It has been three years since I last wrote an update on my PhD. I guess anoth­er post is in order. 

My PhD plan was for­mal­ly green-lit in Octo­ber 2019. I am now over three years into this thing. There are rough­ly two more years left on the clock. I update my plans on a rolling basis. By my lat­est esti­ma­tion, I should be ready to request a date for my defense in May 2023. 

Of course, the pan­dem­ic forced me to adjust course. I am lucky enough not to be locked into par­tic­u­lar meth­ods or cas­es that are fun­da­men­tal­ly incom­pat­i­ble with our cur­rent predica­ment. But still, I had to change up my meth­ods, and recon­sid­er the sequenc­ing of my planned studies. 

The con­fer­ence paper I men­tioned in the pre­vi­ous update, using the MX3D bridge to explore smart cities’ log­ic of con­trol and city­ness, was reject­ed by DIS. I per­formed a rewrite, but then came to the con­clu­sion it was kind of a false start. These kinds of things are all in the game, of course.

The sec­ond paper I wrote uses the Trans­par­ent Charg­ing Sta­tion to inves­ti­gate how notions of trans­par­ent AI dif­fer between experts and cit­i­zens. It was final­ly accept­ed late last year and should see pub­li­ca­tion in AI & Soci­ety soon. It is titled Ten­sions in Trans­par­ent Urban AI: Design­ing A Smart Elec­tric Vehi­cle Charge Point. This piece went through mul­ti­ple major revi­sions and was pre­vi­ous­ly reject­ed by DIS and CHI.

A third paper, Con­testable AI by Design: Towards A Frame­work, uses a sys­tem­at­ic lit­er­a­ture review of AI con­testa­bil­i­ty to con­struct a pre­lim­i­nary design frame­work, is cur­rent­ly under review at a major phi­los­o­phy of tech­nol­o­gy jour­nal. Fin­gers crossed.

And cur­rent­ly, I am work­ing on my fourth pub­li­ca­tion, tan­gen­tial­ly titled Con­testable Cam­era Cars: A Spec­u­la­tive Design Explo­ration of Pub­lic AI Sys­tems Respon­sive to Val­ue Change, which will be based on empir­i­cal work that uses spec­u­la­tive design as a way to devel­op guide­lines and exam­ples for the afore­men­tioned design frame­work, and to inves­ti­gate civ­il ser­vants’ views on the path­ways towards con­testable AI sys­tems in pub­lic administration.

Once that one is done, I intend to do one more study, prob­a­bly look­ing into mon­i­tor­ing and trace­abil­i­ty as poten­tial lever­age points for con­testa­bil­i­ty, after which I will turn my atten­tion to com­plet­ing my thesis. 

Aside from my research, in 2021 was allowed to devel­op and teach a mas­ter elec­tive cen­tered around my PhD top­ic, titled AI & Soci­ety. In it, stu­dents are equipped with tech­ni­cal knowl­edge of AI, and tools for think­ing about AI ethics. They apply these to a design stu­dio project focused on con­cep­tu­al­iz­ing a respon­si­ble AI-enabled ser­vice that address­es a social issue the city of Ams­ter­dam might con­ceiv­ably strug­gle with. Stu­dents also write a brief paper reflect­ing on and cri­tiquing their group design work. You can see me on Vimeo do a brief video intro­duc­tion for stu­dents who are con­sid­er­ing the course. I will be run­ning the course again this year start­ing end of February.

I also men­tored a num­ber of bril­liant mas­ter grad­u­a­tion stu­dents: Xueyao Wang (with Jacky Bour­geois as chair) Jooy­oung Park, Loes Sloet­jes (both with Roy Ben­dor as chair) and cur­rent­ly Fabi­an Geis­er (with Euiy­oung Kim as chair). Work­ing with stu­dents is one of the best parts of being in academia.

All of the above would not have been pos­si­ble with­out the great sup­port from my super­vi­so­ry team: Ianus Keller, Neelke Doorn and Gerd Kortuem. I should also give spe­cial men­tion to Thi­js Turel at AMS Institute’s Respon­si­ble Sens­ing Lab, where most of my empir­i­cal work is situated.

If you want to dig a lit­tle deep­er into some of this, I recent­ly set up a web­site for my PhD project over at contestable.ai.

Design and machine learning – an annotated reading list

Ear­li­er this year I coached Design for Inter­ac­tion mas­ter stu­dents at Delft Uni­ver­si­ty of Tech­nol­o­gy in the course Research Method­ol­o­gy. The stu­dents organ­ised three sem­i­nars for which I pro­vid­ed the claims and assigned read­ing. In the sem­i­nars they argued about my claims using the Toul­min Mod­el of Argu­men­ta­tion. The read­ings served as sources for back­ing and evidence.

The claims and read­ings were all relat­ed to my nascent research project about machine learn­ing. We delved into both design­ing for machine learn­ing, and using machine learn­ing as a design tool.

Below are the read­ings I assigned, with some notes on each, which should help you decide if you want to dive into them yourself.

Hebron, Patrick. 2016. Machine Learn­ing for Design­ers. Sebastopol: O’Reilly.

The only non-aca­d­e­m­ic piece in this list. This served the pur­pose of get­ting all stu­dents on the same page with regards to what machine learn­ing is, its appli­ca­tions of machine learn­ing in inter­ac­tion design, and com­mon chal­lenges encoun­tered. I still can’t think of any oth­er sin­gle resource that is as good a start­ing point for the sub­ject as this one.

Fiebrink, Rebec­ca. 2016. “Machine Learn­ing as Meta-Instru­ment: Human-Machine Part­ner­ships Shap­ing Expres­sive Instru­men­tal Cre­ation.” In Musi­cal Instru­ments in the 21st Cen­tu­ry, 14:137–51. Sin­ga­pore: Springer Sin­ga­pore. doi:10.1007/978–981–10–2951–6_10.

Fiebrink’s Wek­ina­tor is ground­break­ing, fun and inspir­ing so I had to include some of her writ­ing in this list. This is most­ly of inter­est for those look­ing into the use of machine learn­ing for design and oth­er cre­ative and artis­tic endeav­ours. An impor­tant idea explored here is that tools that make use of (inter­ac­tive, super­vised) machine learn­ing can be thought of as instru­ments. Using such a tool is like play­ing or per­form­ing, explor­ing a pos­si­bil­i­ty space, engag­ing in a dia­logue with the tool. For a tool to feel like an instru­ment requires a tight action-feed­back loop.

Dove, Gra­ham, Kim Hal­skov, Jodi For­l­izzi, and John Zim­mer­man. 2017. UX Design Inno­va­tion: Chal­lenges for Work­ing with Machine Learn­ing as a Design Mate­r­i­al. The 2017 CHI Con­fer­ence. New York, New York, USA: ACM. doi:10.1145/3025453.3025739.

A real­ly good sur­vey of how design­ers cur­rent­ly deal with machine learn­ing. Key take­aways include that in most cas­es, the appli­ca­tion of machine learn­ing is still engi­neer­ing-led as opposed to design-led, which ham­pers the cre­ation of non-obvi­ous machine learn­ing appli­ca­tions. It also makes it hard for design­ers to con­sid­er eth­i­cal impli­ca­tions of design choic­es. A key rea­son for this is that at the moment, pro­to­typ­ing with machine learn­ing is pro­hib­i­tive­ly cumbersome.

Fiebrink, Rebec­ca, Per­ry R Cook, and Dan True­man. 2011. “Human Mod­el Eval­u­a­tion in Inter­ac­tive Super­vised Learn­ing.” In, 147. New York, New York, USA: ACM Press. doi:10.1145/1978942.1978965.

The sec­ond Fiebrink piece in this list, which is more of a deep dive into how peo­ple use Wek­ina­tor. As with the chap­ter list­ed above this is required read­ing for those work­ing on design tools which make use of inter­ac­tive machine learn­ing. An impor­tant find­ing here is that users of intel­li­gent design tools might have very dif­fer­ent cri­te­ria for eval­u­at­ing the ‘cor­rect­ness’ of a trained mod­el than engi­neers do. Such cri­te­ria are like­ly sub­jec­tive and eval­u­a­tion requires first-hand use of the mod­el in real time. 

Bostrom, Nick, and Eliez­er Yud­kowsky. 2014. “The Ethics of Arti­fi­cial Intel­li­gence.” In The Cam­bridge Hand­book of Arti­fi­cial Intel­li­gence, edit­ed by Kei­th Frank­ish and William M Ram­sey, 316–34. Cam­bridge: Cam­bridge Uni­ver­si­ty Press. doi:10.1017/CBO9781139046855.020.

Bostrom is known for his some­what crazy but thought­pro­vok­ing book on super­in­tel­li­gence and although a large part of this chap­ter is about the ethics of gen­er­al arti­fi­cial intel­li­gence (which at the very least is still a way out), the first sec­tion dis­cuss­es the ethics of cur­rent “nar­row” arti­fi­cial intel­li­gence. It makes for a good check­list of things design­ers should keep in mind when they cre­ate new appli­ca­tions of machine learn­ing. Key insight: when a machine learn­ing sys­tem takes on work with social dimensions—tasks pre­vi­ous­ly per­formed by humans—the sys­tem inher­its its social requirements.

Yang, Qian, John Zim­mer­man, Aaron Ste­in­feld, and Antho­ny Toma­sic. 2016. Plan­ning Adap­tive Mobile Expe­ri­ences When Wire­fram­ing. The 2016 ACM Con­fer­ence. New York, New York, USA: ACM. doi:10.1145/2901790.2901858.

Final­ly, a feet-in-the-mud explo­ration of what it actu­al­ly means to design for machine learn­ing with the tools most com­mon­ly used by design­ers today: draw­ings and dia­grams of var­i­ous sorts. In this case the focus is on using machine learn­ing to make an inter­face adap­tive. It includes an inter­est­ing dis­cus­sion of how to bal­ance the use of implic­it and explic­it user inputs for adap­ta­tion, and how to deal with infer­ence errors. Once again the lim­i­ta­tions of cur­rent sketch­ing and pro­to­typ­ing tools is men­tioned, and relat­ed to the need for design­ers to devel­op tac­it knowl­edge about machine learn­ing. Such tac­it knowl­edge will only be gained when design­ers can work with machine learn­ing in a hands-on manner.

Supplemental material

Floyd, Chris­tiane. 1984. “A Sys­tem­at­ic Look at Pro­to­typ­ing.” In Approach­es to Pro­to­typ­ing, 1–18. Berlin, Hei­del­berg: Springer Berlin Hei­del­berg. doi:10.1007/978–3–642–69796–8_1.

I pro­vid­ed this to stu­dents so that they get some addi­tion­al ground­ing in the var­i­ous kinds of pro­to­typ­ing that are out there. It helps to pre­vent reduc­tive notions of pro­to­typ­ing, and it makes for a nice com­ple­ment to Buxton’s work on sketch­ing.

Ble­vis, E, Y Lim, and E Stolter­man. 2006. “Regard­ing Soft­ware as a Mate­r­i­al of Design.”

Some of the papers refer to machine learn­ing as a “design mate­r­i­al” and this paper helps to under­stand what that idea means. Soft­ware is a mate­r­i­al with­out qual­i­ties (it is extreme­ly mal­leable, it can sim­u­late near­ly any­thing). Yet, it helps to con­sid­er it as a phys­i­cal mate­r­i­al in the metaphor­i­cal sense because we can then apply ways of design think­ing and doing to soft­ware programming.

Design × AI coffee meetup

If you work in the field of design or arti­fi­cial intel­li­gence and are inter­est­ed in explor­ing the oppor­tu­ni­ties at their inter­sec­tion, con­sid­er your­self invit­ed to an infor­mal cof­fee meet­up on Feb­ru­ary 15, 10am at Brix in Amsterdam.

Erik van der Plui­jm and myself have for a while now been car­ry­ing on a con­ver­sa­tion about AI and design and we felt it was time to expand the cir­cle a bit. We are very curi­ous who else out there shares our excitement.

Ques­tions we are mulling over include: How does the design process change when cre­at­ing intel­li­gent prod­ucts? And: How can teams col­lab­o­rate with intel­li­gent design tools to solve prob­lems in new and inter­est­ing ways?

Any­way, lots to chew on.

No need to sign up or any­thing, just show up and we’ll see what happens.

High-skill robots, low-skill workers

Some notes on what I think I under­stand about tech­nol­o­gy and inequality.

Let’s start with an obvi­ous big ques­tion: is tech­nol­o­gy destroy­ing jobs faster than they can be replaced? On the long term the evi­dence isn’t strong. Humans always appear to invent new things to do. There is no rea­son this time around should be any different.

But in the short term tech­nol­o­gy has con­tributed to an evap­o­ra­tion of mid-skilled jobs. Parts of these jobs are auto­mat­ed entire­ly, parts can be done by few­er peo­ple because of high­er pro­duc­tiv­i­ty gained from tech.

While pro­duc­tiv­i­ty con­tin­ues to grow, jobs are lag­ging behind. The year 2000 appears to have been a turn­ing point. “Some­thing” hap­pened around that time. But no-one knows exact­ly what. 

My hunch is that we’ve seen an emer­gence of a new class of pseu­do-monop­o­lies. Oli­gop­o­lies. And this is com­pound­ed by a ‘win­ner takes all’ dynam­ic that tech­nol­o­gy seems to produce. 

Oth­ers have point­ed to glob­al­i­sa­tion but although this might be a con­tribut­ing fac­tor, the evi­dence does not sup­port the idea that it is the major cause.

So what are we left with?

His­tor­i­cal­ly, look­ing at pre­vi­ous tech­no­log­i­cal upsets, it appears edu­ca­tion makes a big dif­fer­ence. Peo­ple neg­a­tive­ly affect­ed by tech­no­log­i­cal progress should have access to good edu­ca­tion so that they have options. In the US the access to high qual­i­ty edu­ca­tion is not equal­ly divided.

Appar­ent­ly fam­i­ly income is asso­ci­at­ed with edu­ca­tion­al achieve­ment. So if your fam­i­ly is rich, you are more like­ly to become a high skilled indi­vid­ual. And high skilled indi­vid­u­als are priv­i­leged by the tech economy.

And if Piket­ty’s is right, we are approach­ing a real­i­ty in which mon­ey made from wealth ris­es faster than wages. So there is a feed­back loop in place which only exac­er­bates the situation.

One more bul­let: If you think trick­le-down eco­nom­ics, increas­ing the size of the pie will help, you might be mis­tak­en. It appears social mobil­i­ty is helped more by decreas­ing inequal­i­ty in the dis­tri­b­u­tion of income growth.

So some pre­lim­i­nary con­clu­sions: a pro­gres­sive tax on wealth won’t solve the issue. The edu­ca­tion sys­tem will require reform, too. 

I think this is the cen­tral irony of the whole sit­u­a­tion: we are work­ing hard to teach machines how to learn. But we are neglect­ing to improve how peo­ple learn.

Adapting intelligent tools for creativity

I read Alper’s book on con­ver­sa­tion­al user inter­faces over the week­end and was struck by this paragraph:

The holy grail of a con­ver­sa­tion­al sys­tem would be one that’s aware of itself — one that knows its own mod­el and inter­nal struc­ture and allows you to change all of that by talk­ing to it. Imag­ine being able to tell Siri to tone it down a bit with the jokes and that it would then actu­al­ly do that.”

His point stuck with me because I think this is of par­tic­u­lar impor­tance to cre­ative tools. These need to be flex­i­ble so that a vari­ety of peo­ple can use them in dif­fer­ent cir­cum­stances. This adapt­abil­i­ty is what lends a tool depth.

The depth I am think­ing of in cre­ative tools is sim­i­lar to the one in games, which appears to be derived from a kind of semi-ordered­ness. In short, you’re look­ing for a sweet spot between too sim­ple and too complex.

And of course, you need good defaults.

Back to adap­ta­tion. This can hap­pen in at least two ways on the inter­face lev­el: modal or mod­e­less. A sim­ple exam­ple of the for­mer would be to go into a pref­er­ences win­dow to change the behav­iour of your draw­ing pack­age. Sim­i­lar­ly, mod­e­less adap­ta­tion hap­pens when you rearrange some pan­els to bet­ter suit the task at hand.

Return­ing to Siri, the equiv­a­lence of mod­e­less adap­ta­tion would be to tell her to tone it down when her sense of humor irks you. 

For the modal solu­tion, imag­ine a humor slid­er in a set­tings screen some­where. This would be a ter­ri­ble solu­tion because it offers a poor map­ping of a con­trol to a per­son­al­i­ty trait. Can you pin­point on a scale of 1 to 10 your pre­ferred amount of humor in your hypo­thet­i­cal per­son­al assis­tant? And any­way, doesn’t it depend on a lot of sit­u­a­tion­al things such as your mood, the par­tic­u­lar task you’re try­ing to com­plete and so on? In short, this requires some­thing more sit­u­at­ed and adaptive. 

So just being able to tell Siri to tone it down would be the equiv­a­lent of rear­rang­ing your Pho­to­shop palets. And in a next inter­ac­tion Siri might care­ful­ly try some humor again to gauge your response. And if you encour­age her, she might be more humor­ous again.

Enough about fun­ny Siri for now because it’s a bit of a sil­ly example.

Fun­ny Siri, although she’s a bit of a Sil­ly exam­ple, does illus­trate anoth­er prob­lem I am try­ing to wrap my head around. How does an intel­li­gent tool for cre­ativ­i­ty com­mu­ni­cate its inter­nal state? Because it is prob­a­bilis­tic, it can’t be eas­i­ly mapped to a graph­ic infor­ma­tion dis­play. And so our old way of manip­u­lat­ing state, and more specif­i­cal­ly adapt­ing a tool to our needs becomes very dif­fer­ent too.

It seems to be best for an intel­li­gent sys­tem to be open to sug­ges­tions from users about how to behave. Adapt­ing an intel­li­gent cre­ative tool is less like rear­rang­ing your work­space and more like coor­di­nat­ing with a coworker. 

My ide­al is for this to be done in the same mode (and so using the same con­trols) as when doing the work itself. I expect this to allow for more flu­id inter­ac­tions, going back and forth between doing the work at hand, and meta-com­mu­ni­ca­tion about how the sys­tem sup­ports the work. I think if we look at how peo­ple col­lab­o­rate this hap­pens a lot, com­mu­ni­ca­tion and meta-com­mu­ni­ca­tion going on con­tin­u­ous­ly in the same channels.

We don’t need a self-aware arti­fi­cial intel­li­gence to do this. We need to apply what com­put­er sci­en­tists call super­vised learn­ing. The basic idea is to pro­vide a sys­tem with exam­ple inputs and desired out­puts, and let it infer the nec­es­sary rules from them. If the results are unsat­is­fac­to­ry, you sim­ply con­tin­ue train­ing it until it per­forms well enough. 

A super fun exam­ple of this approach is the Wek­ina­tor, a piece of machine learn­ing soft­ware for cre­at­ing musi­cal instru­ments. Below is a video in which Wekinator’s cre­ator Rebec­ca Fiebrink per­forms sev­er­al demos.

Here we have an intel­li­gent sys­tem learn­ing from exam­ples. A per­son manip­u­lat­ing data in stead of code to get to a par­tic­u­lar desired behav­iour. But what Wek­ina­tor lacks and what I expect will be required for this type of thing to real­ly catch on is for the train­ing to hap­pen in the same mode or medi­um as the per­for­mance. The tech­nol­o­gy seems to be get­ting there, but there are many inter­ac­tion design prob­lems remain­ing to be solved. 

Generating UI design variations

AI design tool for UI design alternatives

I am still think­ing about AI and design. How is the design process of AI prod­ucts dif­fer­ent? How is the user expe­ri­ence of AI prod­ucts dif­fer­ent? Can design tools be improved with AI?

When it comes to improv­ing design tools with AI my start­ing point is game design and devel­op­ment. What fol­lows is a quick sketch of one idea, just to get it out of my system.

Mixed-ini­tia­tive’ tools for pro­ce­dur­al gen­er­a­tion (such as Tana­gra) allow design­ers to cre­ate high-lev­el struc­tures which a machine uses to pro­duce full-fledged game con­tent (such as lev­els). It hap­pens in a real-time. There is a con­tin­u­ous back-and-forth between design­er and machine.

Soft­ware user inter­faces, on mobile in par­tic­u­lar, are increas­ing­ly fre­quent­ly assem­bled from ready-made com­po­nents accord­ing to more or less well-described rules tak­en from design lan­guages such as Mate­r­i­al Design. These design lan­guages are cur­rent­ly pri­mar­i­ly described for human con­sump­tion. But it should be a small step to make a design lan­guage machine-readable.

So I see an oppor­tu­ni­ty here where a design­er might assem­ble a UI like they do now, and a machine can do sev­er­al things. For exam­ple it can test for adher­ence to design lan­guage rules, sug­gest cor­rec­tions or even auto-cor­rect as the design­er works.

More inter­est­ing­ly, a machine might take one UI mock­up, and pro­vide the design­er with sev­er­al more pos­si­ble vari­a­tions. To do this it could use dif­fer­ent lay­outs, or alter­na­tive com­po­nents that serve a same or sim­i­lar pur­pose to the ones used. 

In high pres­sure work envi­ron­ments where time is scarce, cor­ners are often cut in the diver­gence phase of design. Machines could aug­ment design­ers so that gen­er­at­ing many design alter­na­tives becomes less labo­ri­ous both men­tal­ly and phys­i­cal­ly. Ide­al­ly, machines would sur­prise and even inspire us. And the final say would still be ours.

Artificial intelligence as partner

Some notes on arti­fi­cial intel­li­gence, tech­nol­o­gy as part­ner and relat­ed user inter­face design chal­lenges. Most­ly notes to self, not sure I am adding much to the debate. Just sum­maris­ing what I think is impor­tant to think about more. Warn­ing: Dense with links.

Matt Jones writes about how arti­fi­cial intel­li­gence does not have to be a slave, but can also be partner.

I’m per­son­al­ly much more inter­est­ed in machine intel­li­gence as human aug­men­ta­tion rather than the oft-hyped AI assis­tant as a sep­a­rate embodiment.

I would add a third pos­si­bil­i­ty, which is AI as mas­ter. A com­mon fear we humans have and one I think only grow­ing as things like Alpha­Go and new Boston Dynam­ics robots keep happening.

I have had a tweet pinned to my time­line for a while now, which is a quote from Play Mat­ters.

tech­no­logy is not a ser­vant or a mas­ter but a source of expres­sion, a way of being” 

So this idea actu­al­ly does not just apply to AI but to tech in gen­er­al. Of course, as tech gets smarter and more inde­pen­dent from humans, the idea of a ‘third way’ only grows in importance. 

More tweet­ing. A while back, short­ly after AlphaGo’s vic­to­ry, James tweet­ed:

On the one hand, we must insist, as Kas­parov did, on Advanced Go, and then Advanced Every­thing Else https://en.wikipedia.org/wiki/Advanced_Chess

Advanced Chess is a clear exam­ple of humans and AI part­ner­ing. And it is also an exam­ple of tech­nol­o­gy as a source of expres­sion and a way of being.

Also, in a WIRED arti­cle on Alpha­Go, some­one who had played the AI repeat­ed­ly says his game has improved tremendously. 

So that is the promise: Arti­fi­cial­ly intel­li­gent sys­tems which work togeth­er with humans for mutu­al benefit. 

Now of course these AIs don’t just arrive into the world ful­ly formed. They are cre­at­ed by humans with par­tic­u­lar goals in mind. So there is a design com­po­nent there. We can design them to be part­ners but we can also design them to be mas­ters or slaves.

As an aside: Maybe AIs that make use of deep learn­ing are par­tic­u­lar­ly well suit­ed to this part­ner mod­el? I do not know enough about it to say for sure. But I was struck by this piece on why Google ditched Boston Dynam­ics. There appar­ent­ly is a sig­nif­i­cant dif­fer­ence between holis­tic and reduc­tion­ist approach­es, deep learn­ing being holis­tic. I imag­ine reduc­tion­ist AI might be more depen­dent on humans. But this is just wild spec­u­la­tion. I don’t know if there is any­thing there.

This insis­tence of James on “advanced every­thing else” is a world view. A pol­i­tics. To allow our­selves to be increas­ing­ly entan­gled with these sys­tems, to not be afraid of them. Because if we are afraid, we either want to sub­ju­gate them or they will sub­ju­gate us. It is also about not obscur­ing the sys­tems we are part of. This is a sen­ti­ment also expressed by James in the same series of tweets I quot­ed from earlier:

These emer­gences are also the best mod­el we have ever built for describ­ing the true state of the world as it always already exists.

And there is over­lap here with ideas expressed by Kevin in ‘Design as Par­tic­i­pa­tion’:

[W]e are no longer just using com­put­ers. We are using com­put­ers to use the world. The obscured and com­plex code and engi­neer­ing now engages with peo­ple, resources, civics, com­mu­ni­ties and ecosys­tems. Should design­ers con­tin­ue to priv­i­lege users above all oth­ers in the sys­tem? What would it mean to design for par­tic­i­pants instead? For all the participants?

AI part­ners might help us to bet­ter see the sys­tems the world is made up of and engage with them more deeply. This hope is expressed by Matt Webb, too:

with the re-emer­gence of arti­fi­cial intel­li­gence (only this time with a bud­dy-style user inter­face that actu­al­ly works), this ques­tion of “doing some­thing for me” vs “allow­ing me to do even more” is going to get even more pro­nounced. Both are effec­tive, but the first sucks… or at least, it sucks accord­ing to my own per­son­al pol­i­tics, because I regard indi­vid­ual alien­ation from soci­ety and com­plex sys­tems as one of the huge threats in the 21st century.

I am remind­ed of the mixed-ini­tia­tive sys­tems being researched in the area of pro­ce­dur­al con­tent gen­er­a­tion for games. I wrote about these a while back on the Hub­bub blog. Such sys­tems are part­ners of design­ers. They give some­thing like super pow­ers. Now imag­ine such pow­ers applied to oth­er prob­lems. Quite exciting.

Actu­al­ly, in the afore­men­tioned arti­cle I dis­tin­guish between tools for mak­ing things and tools for inspect­ing pos­si­bil­i­ty spaces. In the first case design­ers manip­u­late more abstract rep­re­sen­ta­tions of the intend­ed out­come and the sys­tem gen­er­ates the actu­al out­put. In the sec­ond case the sys­tem visu­alis­es the range of pos­si­ble out­comes giv­en a par­tic­u­lar con­fig­u­ra­tion of the abstract rep­re­sen­ta­tion. These two are best paired. 

From a design per­spec­tive, a lot remains to be fig­ured out. If I look at those mixed-ini­tia­tive tools I am struck by how poor­ly they com­mu­ni­cate what the AI is doing and what its capa­bil­i­ties are. There is a huge user inter­face design chal­lenge there. 

For stuff focused on get­ting infor­ma­tion, a con­ver­sa­tion­al UI seems to be the cur­rent local opti­mum for work­ing with an AI. But for tools for cre­ativ­i­ty, to use the two-way split pro­posed by Vic­tor, dif­fer­ent UIs will be required.

What shape will they take? What visu­al lan­guage do we need to express the par­tic­u­lar prop­er­ties of arti­fi­cial intel­li­gence? What approach­es can we take in addi­tion to per­son­i­fy­ing AI as bots or char­ac­ters? I don’t know and I can hard­ly think of any good exam­ples that point towards promis­ing approach­es. Lots to be done.