Suchergebnisse

Voisiko koneellinen tekstituotanto myös lisätä luovuuden arvostusta tieteellisessä kirjoittamisessa?

Generatiivista tekoälyä käytetään tehostamaan tutkimuksen eri vaiheita, mikä on johtanut keskusteluun tekoälyn käytön merkitsemisestä erilaisissa julkaisuissa. Media & viestintä -lehden toimitus on toistaiseksi katsonut, ettei erilliselle tekoälyilmoitukselle ole artikkeleissa tarvetta. Jos tekoäly on kuitenkin avustanut työskentelyä niin, ettei tutkijalla enää ole täyttä kontrollia johonkin työprosessin vaiheeseen, tulee siitä kertoa. Rajapyykit muuttuvat, ja niiden vetäminen ja merkitseminen vaativat jokaiselta tutkijalta harkintaa nyt ja tulevaisuudessa.

Haastattelussa Rafael Grohmannin kanssa Paraná esittelee tutkimusaiheensa ydinkysymykset, aiheen kytkökset kriittiseen tekoälytutkimukseen, infrastruktuurin käsitteen, sekä muita teknologiaa koskevia keskusteluja marxilaisesta näkökulmasta. Haastattelu on julkaistu alun perin englanniksi ja portugaliksi maaliskuussa 2023 DigiLabour-verkkojulkaisussa.

Tässä artikkelissa tarkastelemme sitä, millaisena ja miten tekoäly esitetään suomalaisessa julkisessa keskustelussa, ja ketkä tekoälystä suurelle yleisölle kertovat. Aineistona olemme käyttäneet Yleisradion verkkosivujen tekoälyä käsitteleviä artikkeleja. Tulosten perusteella tekoälystä pääsevät kertomaan useimmin talouden ja teollisuuden aloilla työskentelevät miehet. Aineistossa esiintyneet tekoälytulevaisuuskuvaukset pitäytyvät pitkälti nykyisen länsikeskeisen kapitalistisen maailmankuvan sisällä. Toisin sanoen, ne eivät haastaneet tai ylittäneet vallitsevaa status quoa, vaan näkivät tulevaisuuden pikemminkin lineaarisena kehityksenä nykytilasta. Aiemman tutkimuksen perusteella yleinen käsitys tekoälyn luonteesta ja mahdollisuuksista muodostuu huomattavissa määrin sen perusteella, millaisena tekoäly kuvataan julkisessa keskustelussa. Täten tutkimuksemme peräänkuuluttaa moniäänisyyttä tekoälyn kaltaisesta moniulotteisesta ja kompleksisesta ilmiöstä käytävään julkiseen keskusteluun

There appears to be a common agreement that ethical concerns are of high importance when it comes to systems equipped with some sort of Artificial Intelligence (AI). Demands for ethical AI are declared from all directions. As a response, in recent years, public bodies, governments, and universities have rushed in to provide a set of principles to be considered when AI based systems are designed and used. We have learned, however, that high-level principles do not turn easily into actionable advice for practitioners. Hence, also companies are publishing their own ethical guidelines to guide their AI development. This paper argues that AI software is still software and needs to be approached from the software development perspective. The software engineering paradigm has introduced maturity model thinking, which provides a roadmap for companies to improve their performance from the selected viewpoints known as the key capabilities. We want to voice out a call for action for the development of a maturity model for AI software. We wish to discuss whether the focus should be on AI ethics or, more broadly, the quality of an AI system, called a maturity model for the development of AI systems. ; peerReviewed

Ethical concerns related to Artificial Intelligence (AI) equipped systems are prompting demands for ethical AI from all directions. As a response, in recent years public bodies, governments, and companies have rushed to provide guidelines and principles for how AI-based systems are designed and used ethically. We have learned, however, that high-level principles and ethical guidelines cannot be easily converted into actionable advice for industrial organizations that develop AI-based information systems. Maturity models are commonly used in software and systems development companies as a roadmap for improving the performance. We argue that they could also be applied in the context of developing ethically aligned AI systems. In this paper, we propose a maturity model for AI ethics and explain how it can be devised by using a Design Science Research approach. ; peerReviewed

The Unmanned Aerial Vehicles (UAV) commonly known as drones are currently used in a wide range of operations such as border monitoring, aerial reconnaissance, traffic control and military interventions in armed conflicts. These aerial vehicles are expected to be reliable, automated and sometimes autonomous machines, albeit the human factor continues to play a crucial role in programming and control. At their genesis, drones were complex, large and reserved to an exclusive club of technologically advanced military powers. They tended to be used against technologically weak military targets. Developments in the price, size and sophistication of drones has now enabled almost anyone to purchase them. These contemporary machines are often small, and, with increasing usage of Artificial Intelligence (AI) has put them in the cost and usage range of almost any combatant. Therefore, there is a need to re-think strategies and tactics for their use. These 'mini-drones' rarely have the sophistication and capabilities of 'conventional' drones, but they do have the ability to provide an asset in large numbers and increasing capabilities. Although they might not have such attributes as the lifting capabilities of the larger models, they can be used economically and en masse and so have can have a different but equally effective outcomes. This paper examines the swarming and associated abilities to overwhelm a combatant as well as bring extra functionality by means of extra sensors spread throughout the swarm. Thus, sophisticated AI provides a swarm with various types of functionality to the drones: for instance, dummy/distraction drones, kinetic and non-kinetic attack drones, surveillance drones as well as drones that can be equipped with wireless access points and deployed to configure an ad-hoc flying network. This paper also examines UAV/drone categories and autonomy and also how autonomy and Swarm intelligence (SI) can be used to create efficiency for a variety of operation concepts. ; peerReviewed

Various recent Artificial Intelligence (AI) system failures, some of which have made the global headlines, have highlighted issues in these systems. These failures have resulted in calls for more ethical AI systems that better take into account their effects on various stakeholders. However, implementing AI ethics into practice is still an on-going challenge. High-level guidelines for doing so exist, devised by governments and private organizations alike, but lack practicality for developers. To address this issue, in this paper, we present a method for implementing AI ethics. The method, ECCOLA, has been iteratively developed using a cyclical action design research approach. The method aims at making the high-level AI ethics principles more practical, making it possible for developers to more easily implement them in practice. ; peerReviewed

This study uses a design science research methodology to develop and evaluate the Pi-Mind agent, an information technology artefact that acts as a responsible, resilient, ubiquitous cognitive clone – or a digital copy – and an autonomous representative of a human decision-maker. Pi-Mind agents can learn the decision-making capabilities of their "donors" in a specific training environment based on generative adversarial networks. A trained clone can be used by a decision-maker as an additional resource for one's own cognitive enhancement, as an autonomous representative, or even as a replacement when appropriate. The assumption regarding this approach is as follows: when someone was forced to leave a critical process because of, for example, sickness, or wanted to take care of several simultaneously running processes, then they would be more confident knowing that their autonomous digital representatives were as capable and predictable as their exact personal "copy". The Pi-Mind agent was evaluated in a Ukrainian higher education environment and a military logistics laboratory. In this paper, in addition to describing the artefact, its expected utility, and its design process within different contexts, we include the corresponding proof of concept, proof of value, and proof of use. ; peerReviewed

Abstract. This paper focuses on how major national strategies call attention to the human dimensions of artificial intelligence (AI). All intelligent technologies using AI are constructed for people as either active users or as relatively passive target persons. Thus, human properties and human research should have an important role in developing future AI systems. In these development strategies, it is interesting to pay attention to the underlying intuitive assumptions and tacit commitments. This issue is especially interesting when we think about what governmental working groups say about people and their changing lives in their strategies. The traditional stances adopted in writing national strategies, in which technology development is seen as a purely technical issue, should be challenged. In the end, by putting human dimensions aside, societies cannot prepare themselves for the transformation risks. It is also probable that this stance makes communication between technical and human research more difficult. Keywords: AI-strategies · Human factors · Social transformation ; peerReviewed

Kiinnostus tekoälyä kohtaan on lisääntynyt terveydenhuollossa ja sitä koskevaa tutkimusta on tehty jonkin verran esimerkiksi terveysriskien ja riskitekijöiden tunnistamiseksi, lääkitysten arvioimiseksi sekä sensoreiden tuottamien tulosten arvioimiseksi. Tämän integroivan kirjallisuuskatsauksen tarkoituksena oli kuvata aikaisemman kirjallisuuden perusteella, miten tekoälyä on hyödynnetty terveydenhuollossa erilaisten terveysriskien tunnistamiseksi ja riskien terveysvaikutusten ennustamiseksi. Katsauksen tavoitteena oli lisätä ymmärrystä tekoälyn hyödyntämismahdollisuuksista ennaltaehkäisevän terveydenhuollon näkökulmasta ja tunnistaa sen mahdollisuuksia myös työterveyshuollossa. Katsausta varten tehtiin hakuja kansainvälisiin tietokantoihin pyrkien integroivan kirjallisuuskatsauksen menetelmin tuomaan uutta näkökulmaa viimeisen viiden vuoden ajalta tekoälyn hyödyntämisestä erityisesti terveysriskien tunnistamiseksi ja ennustamiseksi.
Tämän integroivan kirjallisuuskatsauksen perusteella tekoälyä on hyödynnetty erilaisten terveysriskien ja riskitekijöiden tunnistamiseksi hyödyntämällä potilastietojärjestelmistä ja muista terveydenhuollon järjestelmistä saatavaa rakenteista tai tekstimuotoista tietoa. Mallien rakentamisessa on käytetty useita eri menetelmiä tai niiden yhdistelmiä. Mallien hyvyyden arvioiminen eri menetelmien ja eri riskitekijöiden välillä on haasteellista, mutta tutkimustulokset ovat lupaavia ja osoittavat tekoälyn tuovan uusia mahdollisuuksia erilaisten riskitekijöiden tunnistamiseen sekä tunnistamiseen perustuvien ennustemallien rakentamiseen.
Tekoäly mahdollistaa uudenlaisten potilastietoa hyödyntävien menetelmien kehittämisen terveydenhuollon käyttöön. Erilaiset tekoälyyn perustuvat mallit terveysriskien ja riskitekijöiden tunnistamiseksi ja ennustamiseksi tukevat osaltaan ennaltaehkäisevän terveydenhuollon kehittämistä. Tutkimusta tarvitaan kuitenkin lisää erilaisten menetelmien hyvyyden arvioimiseksi. Tekoälyn hyödyntäminen riskitekijöiden tunnistamiseksi ja ennustemallien luomiseksi edellyttää myös eettistä keskustelua uudenlaisten menetelmien käyttämisessä.

Algorithms are a ubiquitous part of organizations as they enable, guide, and restrict organizing at the level of everyday interactions. This essay focuses on algorithms and organizing by reviewing the literature on algorithms in organizations, examining the viewpoint of relationality and relational agency on algorithms and organizing, exploring the properties of algorithms, and concluding what these mean from an organizational communication viewpoint. Algorithms need data to be collected. The data are always biased, and algorithms exclude everything that is not in their code. They define what is seen as important. Their operating principles are opaque, and they are political due to human interference. Algorithms are not just used. Rather, they are co-actors in organizing. We argue that algorithms demand rethinking communication in the communicative constitution of organizations and call for more empirical research emphasizing the properties of algorithms, the relationality of algorithms, and the temporality of the materialization of algorithms. ; peerReviewed

Many technological innovations have led to the emergence of the platform economy in recent years. This development is changing the entire landscape of business in the era of digitalisation. However, the impacts of the platform economy on public services and government are not well known. In this article we study the potential for the digital platform economy to help restructure the public sector. Firstly, central features of the new platform technology are explored, pointing to an algorithmic revolution, big data and cloud computing. Platforms are used in coordinating market transactions in an extremely efficient way. In order to apply the platform-concept to the public sector, an experimental approach is needed; public platforms cannot be built by transposing mechanical models of the private sector to the public sector, because the market logic of public services is quite different than open markets. To illustrate the challenges and possibilities of the platform economy we explored a few cases from Finland such as 'Suomi.fi' digital service platform and its background technology, which is based on a national architecture for digital services developed in Finland applying X-Road technology created originally in Estonia. As a special case, we studied the Finnish solution to the digital health care system. The case of 'Kanta Services' exemplifies the challenge to simultaneously develop open and secure data systems for health care. Finally, we point out the importance of citizencentred approaches in developing platforms for the public sector. ; nonPeerReviewed

A vast variety of artificial intelligence techniques have been deployed to specific healthcare problems during the last thirty years with varying levels of success while there is a shortage of systematic matching of AI capabilities with the breadth of application opportunities. In this paper, we describe the process of identifying opportunities for deploying artificial intelligence to healthcare and social services on regional and national levels in Finland. The project involved a large number of stakeholders from a variety of backgrounds ranging from governmental agencies to entrepreneurs. The process described includes idea generation of an application or solution and its elaboration in workshops using a design thinking method. The resulting idea pool was filtered down to 34 best use case descriptions, which went through an architectural design process identifying AI capabilities needed in the components of these designs reported in this paper. The potential ones of the use cases were selected for prototype development. The subsequent steps in the process include feasibility prototypes and evaluation of the economic and business value of the solutions and applications. ; peerReviewed

In this edited book, the authors seek answers to the question "What is the future of working life?" with research-based approaches. The state of working life and its changes and development are affected by many factors starting from global phenomena to workplace level events, actions and procedures. The book examines working life trends on multiple levels covering structural development paths, the role of labor policy, organizational practices and subjective experiences of individuals. Working life research produces valuable information which is beneficial both at workplace level and at the use of decision-makers who regulate working life matters. - Artikkelikokoelmassa etsitään vastausta kysymykseen "Mihin työelämä on menossa?" lujasti tutkimuksiin tukeutuen. Työelämän tilaan, muutoksiin ja kehittymiseen vaikuttavat monet tekijät lähtien globaaleista ilmiöistä työpaikkakohtaisiin tapahtumiin ja toimenpiteisiin. Kirja tarkastelee työelämän kehitysilmiöitä monitasoisesti kattaen niin muutoksen rakenteelliset kehityskulut, työvoimapolitiikan roolin, organisaatiokohtaiset käytännöt kuin yksilöiden kokemuksetkin. Kirjassa on vahvasti mukana hyödyntämisnäkökulma. Työelämän tutkimus tuottaa arvokasta tietoa niin työpaikoille kuin työelämäasioita käsitteleville päätöksentekijöille.