A. I. Utilization in the Construction Business: A review on present state and potential for Elenia Oy

The thesis examines the present applications of artificial intelligence in the construction busi-ness domain. Nowadays, businesses are focusing on the safety of an operating environment. In a project-based business, managing projects and portfolios with safety management is significantly important. Lack of knowledge is rarely a root cause of undesired deviations. More often, the deviations in processes are related to an irregularity in compliance with the instructions and rules. With the assistance of AI-based tools, such as machine learning, one can improve efficiency on safety and project management tasks. The thesis provides a gen-eral view of artificial intelligence and a review of present approaches on AI utilization in the construction domain. Also, the thesis suggests the next steps for the utilization of AI in Elenia’s construction business. The first section of the thesis gives an overall view of artificial intelligence. In the second and third sections, a review of the present utilization approaches is examined. In the second section, the utilization is examined in the construction site safety domain. In the third section the examined field is related to the project management do-main. The most common way to utilize AI were to exploit existing data for risk prediction and relationship detection. The risks differ from the examined domain. Thus, building a machine learning model is use-case related. There are various ways to utilize different models to achieve the benefits of machine learning. In Elenia Oy’s activities managing projects have a key role for achieving company’s mission: Electrifying life. The electric grids demand continu-ous maintenance and consistent development. One part of the development is replacement of components that have reached end of the technical lifecycle. For example, replacement can be executed in Elenia’s Säävarma projects. The development of occupational safety Elenia together with its partners has committed for safety manifesto. The key theme of safe-ty manifesto is to render everyone related to Elenia’s work field to return home in good health. The key approach of thesis was to find widely different approaches to utilize an AI for the development of safety and project objectives.Tässä diplomityössä selvitettiin rakentamisliiketoimintoihin liittyviä tekoälyn käyttökohteita. Nykyisin liiketoiminnoissa keskitytään operatiivisten toimintojen turvallisuuteen. Projektiliike-toiminnassa projektien ja portfolioiden johtaminen yhdessä turvallisuusjohtamisen kanssa on huomattavan tärkeää. Tiedon puute on harvoin juurisyy ei-toivotuille poikkeamille. Useammin poikkeamat prosesseissa johtuvat epäsäännöllisyydestä ohjeistuksien ja sääntöjen noudatta-misen suhteen. Tekoälyyn pohjautuvien työkalujen, kuten koneoppiminen, avulla on mahdol-lista kehittää turvallisuuteen ja projektijohtamiseen liittyvien tehtävien tehokkuutta. Tutkielma sisältää yleisen katsauksen tekoälyyn ja tarkastelun nykyisistä lähestymistavoista tekoälyn hyödyntämiseen rakentamisliiketoimintoihin liittyen. Lisäksi työssä muodostetaan ehdotukset tuleville vaiheille tekoälyn hyödyntämiseen Elenian rakentamisliiketoiminnassa. Ensimmäisessä osassa käydään läpi yleiskatsaus tekoälyyn liittyen. Toisessa ja kolmannessa osassa työtä tar-kastellaan nykyisiä tekoälyn käyttökohteita. Toisessa osassa tarkastellaan rakentamistöiden turvallisuuteen liittyviä hyödyntämiskohteita. Kolmannessa osassa vastaava tarkastelu keskit-tyy projekti ja portfoliojohtamisen toimintaympäristöön. Yleisin tapa hyödyntää tekoälyä on selvittää ja tunnistaa toimintaympäristön riskeihin liittyvien tekijöiden suhteita toisiinsa. Erilai-sissa toimintaympäristöissä on erilaisia riskejä, joiden esiintymisen todennäköisyyttä on syytä pienentää. Koneoppimismallien rakentamisen toteutus on käyttökohde sidonnainen, joten on monia tapoja hyödyntää koneoppimista. Elenia Oy:n toiminnassa projektit ja niiden hallinta ovat keskeisessä osassa mahdollistamassa yhtiön missiota: Elämää sähköistämässä. Sähköver-kot vaativat jatkuvaa kunnossapitoa ja johdonmukaista kehittämistä. Osa tätä kehittämistä on teknisen käyttöiän saavuttaneiden komponenttien uusinta, esimerkiksi Elenian Säävarma-hankkeissa. Työturvallisuuden edistämiseksi Elenia on yhdessä kumppaniensa kanssa allekir-joittanut Turvallisuusmanifestin, jonka keskeinen teema on mahdollistaa kaikkien Elenian töissä olevien henkilöiden turvallisen palaamisen terveenä kotiin. Tutkielman keskeisenä lähestymis-tapana oli etsiä laajasti erilaisia tapoja hyödyntää tekoälyä liittyen turvallisuus- ja projektita-voitteiden kehittämiseen

A 4-SEASON LONGITUDINAL STUDY EXAMINING THE ASSOCIATION BETWEEN SEASONALITY, THE BUILT ENVIRONMENT AND SEDENTARY TIME IN 9-14 YEAR OLD CHILDREN LIVING IN A MID-WESTERN CANADIAN CITY

Background: Canadian youth spend on average 8.6 waking hours of their day in a sedentary state, and consistently exceed recommended leisure hour screen-based sedentary limits of two hours per day. Sedentary behaviour (SB) is associated with an increased risk of overweight, obesity, cardiovascular disease and all-cause mortality. Understanding how the built environment and urban design may influence children’s sedentary time (SED), in different social and physical contexts, addresses a significant gap in the scientific literature and contributes to promoting health in children. Research Aim: This work seeks to examine how seasonal changes affect weekday school hour, leisure hour, total daily and location-specific SED in children and if this relationship is moderated by urban design. The relationship between SED and parental support or children’s perception of this support and seasonality are also explored. Research Questions: (1) How do seasonal changes affect SED accumulation in children? (2) Are seasonal changes in SED moderated by neighbourhood built environment (BE)? and (3) Is parental support or children’s perception of this support associated with activity behaviour outcomes in children? Methodology: Families with children aged 9-14 years were recruited from the prairie city of Saskatoon, Canada. Location-specific, device-based SED was captured in children during three time frames over one year using GPS data loggers and accelerometers. Neighbourhood-level BE features were assessed using multiple audit tools and neighbourhood era design. Using a random intercept model, a multilevel modelling approach was taken to understand the relationship between seasons, demographic factors, BE and SED of children. Multilevel model outcomes were stratified by time- (total daily, leisure hour, school hour) and location-dependent SED (home, school, school park and park area). Results: In multilevel models predicting SED outcomes, older children, those with obesity and children with decreased levels of moderate-to-vigorous physical activity consistently accumulated greater levels of SED. Over a child’s entire day, and while at home or in school, children were significantly less sedentary in fall months but more sedentary in spring (vs winter) months. Neighbourhood-level pedestrian access and traffic safety in a child’s home neighbourhood and safety from crime and traffic and universal accessibility in a child’s school neighbourhood moderated the predicted effect of season on children’s SED. Children who perceived screen time limitations by their parents accumulated significantly lower levels of SED and higher levels of MVPA year round. Similarly, children with parents who reported regulating screen time in their children accumulated significantly lower levels of SED and higher levels of MVPA year round. Project Significance: This study provides greater and more nuanced detail about BE, season and sedentariness/activity in children living in a city with four distinct seasons. This new-found understanding of children’s activity behaviours could shape infill and new urban development projects by providing necessary information to relevant public health policy architects, driving urban transformation and healthier cities year-round

Schooling in Developing Countries: The Roles of Supply, Demand and Government Policy

In developing countries, rising incomes, increased demand for more skilled labor, and government investments of considerable resources on building and equipping schools and paying teachers have contributed to global convergence in enrollment rates and completed years of schooling. Nevertheless, in many countries substantial education gaps persist between rich and poor, between rural and urban households and between males and females. To address these gaps, some governments have introduced school vouchers or cash transfers programs that are targeted to disadvantaged children. Others have initiated programs to attract or retain students by expanding school access or by setting higher teacher eligibility requirements or increasing the number of textbooks per student. While enrollments have increased, there has not been a commensurate improvement in knowledge and skills of students. Establishing the impact of these policies and programs requires an understanding of the incentives and constraints faced by all parties involved, the school providers, the parents and the children. The chapter reviews the economic literature on the determinants of schooling outcomes and schooling gaps with a focus on static and dynamic household responses to specific policy initiatives, perceived economic returns and other incentives. It discusses measurement and estimation issues involved with empirically testing these models and reviews findings. Governments have increasingly adopted the practice of experimentation and evaluation before taking steps to expand new policies. Often pilot programs are initiated in settings that are atypically appropriate for the program, so that the results overstate the likely impact of expanding the program to other settings. Program expansion can also result in general equilibrium feedback effects that do not apply to isolated pilots. These behavioral models provide a useful context within which to frame the likely outcomes of such expansion.

Voluntary Public Unemployment Insurance

Voluntary public unemployment systems are limited to a handful of countries, including Finland, Sweden, and, more substantially, Denmark. A voluntary system has the positive feature of other user-cost schemes, potentially efficient targeting of services. This presumes rational behavior as well as reasonable risk rating of premiums and the absence of worker access to alternative social programs. Using a 10 per cent sample of the Danish population drawn from administrative data, we exploit the voluntary Danish system to explore the structure of unemployment insurance demand. The insurance take-up rate is surprisingly high, 80 percent in 1995, but varies systematically with economic incentives in a way that raises doubts about the targeting value of the current system. Political support for the Danish system may derive instead from the fact that a universal, compulsory system would generate rather modest additional net funds and with a twist--additional revenue would come disproportionately from low-wage workers.

Artificial Intelligence-based Smarter Accessibility Evaluations for Comprehensive and Personalized Assessment

The research focuses on utilizing artificial intelligence (AI) and machine learning (ML) algorithms to enhance accessibility for people with disabilities (PwD) in three areas: public buildings, homes, and medical devices. The overarching goal is to improve the accuracy, reliability, and effectiveness of accessibility evaluation systems by leveraging smarter technologies. For public buildings, the challenge lies in developing an accurate and reliable accessibility evaluation system. AI can play a crucial role by analyzing data, identifying potential barriers, and assessing the accessibility of various features within buildings. By training ML algorithms on relevant data, the system can learn to make accurate predictions about the accessibility of different spaces and help policymakers and architects design more inclusive environments. For private places such as homes, it is essential to have a person-focused accessibility evaluation system. By utilizing machine learning-based intelligent systems, it becomes possible to assess the accessibility of individual homes based on specific needs and requirements. This personalized approach can help identify barriers and recommend modifications or assistive technologies that can enhance accessibility and independence for PwD within their own living spaces. The research also addresses the intelligent evaluation of healthcare devices in the home. Many PwD rely on medical devices for their daily living, and ensuring the accessibility and usability of these devices is crucial. AI can be employed to evaluate the accessibility features of medical devices, provide recommendations for improvement, and even measure their effectiveness in supporting the needs of PwD. Overall, this research aims to enhance the accuracy and reliability of accessibility evaluation systems by leveraging AI and ML technologies. By doing so, it seeks to improve the quality of life for individuals with disabilities by enabling increased independence, fostering social inclusion, and promoting better accessibility in public buildings, private homes, and medical devices

Actuarial Injustice: Discrimination in Crime Prediction Software

The actuarial justice movement has propagated an unprecedented increase in the use of crime prediction software in the criminal justice system. Specifically, two forms of crime prediction software - predictive policing and risk assessment instruments – are now informing high-stakes police and judicial decisions that have direct consequences on individual’s civil rights. While advocates claim that the software can alleviate human biases in the system, critics believe it may actually exacerbate them. Due to the conflicting definitions of fairness across legal, technical, and statistical disciplines, there has been no consensus on the software’s potential for discrimination. In order to demonstrate how discrimination can manifest in crime prediction software, I examined a risk assessment instrument designed to predict pretrial felony rearrest for racial discrimination. The instrument is currently used in New York City and to date, has never been independently reviewed. I found that while the instrument demonstrates acceptable predictive validity for all racial subgroups, black defendants receive significantly higher scores on average than white defendants. Although there were small effect sizes, these differences may transcend into discrimination via disparate impact. Most noteworthy, I discovered that only one of the eight predictor variables in the model - whether or not a defendant had any prior arrests - was significantly predictive of future re-arrest. In fact, a redesigned model that predicts rearrest based solely on a defendant’s number of prior arrests performed just as well as the original model. These findings indicate that crime prediction software that utilizes police-generated data to predict police-dependent outcomes is ultimately predicting police activity, not crime. I proffer that this problem is related to the outcome variable at hand and cannot be sufficiently minimized by data manipulation. Therefore, the police and judicial biases that have always plagued America’s criminal justice system will be paralleled in crime prediction software

Machine Decisions and Human Consequences

As we increasingly delegate decision-making to algorithms, whether directly or indirectly, important questions emerge in circumstances where those decisions have direct consequences for individual rights and personal opportunities, as well as for the collective good. A key problem for policymakers is that the social implications of these new methods can only be grasped if there is an adequate comprehension of their general technical underpinnings. The discussion here focuses primarily on the case of enforcement decisions in the criminal justice system, but draws on similar situations emerging from other algorithms utilised in controlling access to opportunities, to explain how machine learning works and, as a result, how decisions are made by modern intelligent algorithms or 'classifiers'. It examines the key aspects of the performance of classifiers, including how classifiers learn, the fact that they operate on the basis of correlation rather than causation, and that the term 'bias' in machine learning has a different meaning to common usage. An example of a real world 'classifier', the Harm Assessment Risk Tool (HART), is examined, through identification of its technical features: the classification method, the training data and the test data, the features and the labels, validation and performance measures. Four normative benchmarks are then considered by reference to HART: (a) prediction accuracy (b) fairness and equality before the law (c) transparency and accountability (d) informational privacy and freedom of expression, in order to demonstrate how its technical features have important normative dimensions that bear directly on the extent to which the system can be regarded as a viable and legitimate support for, or even alternative to, existing human decision-makers