Voice Command Devices (VCD) such as the ubiquitous Amazon Echo have entered the lives and homes of people the world over. Although emotional recognition and digital empathy are idealistic goals within the development of AI and intelligent agents, the current technology available lacks outward emotional understanding and the personas contribute only Alexithymic (no understanding of emotion) responses. Despite extensive research by large multinational technological organizations, authentic human-like empathic interactions with intelligent agents have not yet been achieved. Consequently, users are lulled into a false sense of security where they believe that their emotions remain private. This paper determines that despite Alexa's demonstrated lack of emotion and emotional understanding, Voice Command Devices such as the Amazon Echo have the ability to deduce emotions such as sadness through inferential data. This is displayed through responses to questions that offer the same information as those posed by health practitioners to establish potential cases of depression. This type of data paves the way for parent companies to effectively target future advertising and build EMOTOgraphic models. As users are presented with no indication of this by intelligent agents, most would be unaware that combined inferential data could be so revealing and potentially extremely profitable from a sales and marketing perspective. This potentially leads to great ethical and privacy concerns as intelligent agents such as Ale xa are gradually and incrementally cured of Alexithymia indicators.
Emotion recognition and digital empathy are visionary goals of research conducted
within the areas of Human Computer Interaction (HCI) and Affective Computing [
VCDs offer a new paradigm for mainstream human interaction with artificial
intelligence. These devices are becoming increasingly commonplace in domestic
environments [
This section provides background for the research, offering information that relates to the disciplines, technologies, models, associations and considerations concerned. 2.1
Affective computing is an emerging interdisciplinary research area that combines
various fields, ranging from artificial intelligence and natural language processing, to
cognitive and social sciences [
Becoming increasingly popular, these devices are typically interactive and function
to gather metrics and support many aspects our lives [
The Amazon Echo provides an artificially intelligent personal assistant referred to
as ‘Alexa’, who possesses a range of functionality and ‘skills’ which enable voice
activation and communication with numerous IoT devices. These include wearable health
devices, shopping lists, streamed music services and calendars, amongst others [
Emotions are physiological, behavioural, and/or communicative reactions to stimuli
that are cognitively processed and experienced [
Emotion recognition describes the process of identifying emotion in humans. The
cognitive mechanisms invoked in recognizing human emotion have been studied for
hundreds of years. The fields of psychology and more recently those such as cognitive
science, have developed a range of models in this area. The most recent models rely
on AI through use of signal processing, machine learning, natural language processing
(NLP) and other modern implementations [
Within emotional recognition, signals detected by all five senses (sight, sound,
touch, scent and taste) may be recruited to enable one human to recognize, process and
understand the emotions of another human. However, typically the signals that are
relied upon most heavily are those that are visual and produce sound [
Words Used
Body Language
Facial Expression
Voice Characteristics Context Awareness
Prior
Experience
Senses Understand
Emotion
Currently, there is significant research in progress that focuses primarily on facial
expressions, NLP and voice characteristics in a bid to help machines to learn to achieve
emotion detection and recognition in a way that emulates human ability [
Replicating the emotion detection capability of people within intelligent agents such
as Alexa is an increasingly popular and innovative field of research [
The majority of VCD devices are limited to speech which allows for NLP of the words spoken and analysis of the voice characteristics. This may be achieved by processing the harvested data in the Cloud via a combination of “Big Data” sets, ML algorithms and the vast Compute Power resources available as depicted in Figure 4.
By enabling VCDs to act as hub for IoT devices and a myriad of applications, the manufacturers have gained access to a rich tapestr y of data that relates to an individual’s emotional state. Once collated and analysed, this data enables emotional state inference on a scale heretofore unknown. Furthermore, the data available to these data hungry organisations is increasing exponentially with the increased popularity of these voice command and IoT devices.
The parent companies such as Amazon who manufacture these devices are primarily
‘sales organisations’ and it is in their interests to market effectively. A good sales
person knows their customer and the more they know, the higher the probability of making
a sale [
Emotional state and mental health have become a key focus for health organisations,
governments and various other organisations in the last decade [
Currently verbal responses from VCDs like Amazon’s Echo do not give an indication that the devices understand emotion in humans. However, when analysis is performed on the intonation of verbal communication and data gathered from searches and linked devices such as Fitbit or music selected through streaming services, patterns may be detected that give insight to the emotional state of the user. Despite the unemotional nature of the device itself, this type of inferential data may still be collected by parent companies for use in the development of ‘Affective’ devices. Additionally. ‘state-ofmind’ has the capacity to greatly impact the needs, behaviour and habits of individuals. No matter how arbitrary, this type of data is valuable and sold to other various entities for purposes such as targeted marketing.
The target VCD was an Amazon Echo that had been configured for a domestic
environment and was interacted with by a single primary user over a period of five
months. This device has also been configured to link to a range of external applications,
as reported in [
For the purposes of this paper it was decided to focus on sadness, one of the most common emotions that potentially may lead to an active condition of depression. To elicit information which would indicate potential levels of depression, a short experiment was conducted. A script was developed which outlined queries to be posed to the Amazon Echo VCD. These questions were designed to:
a) Establish Alexa’s current ‘knowledge’ relating to the primary users’ levels of sadness and depression. This was done in order to document verbal responses that would be offered to the same type of direct questions that would be posed by a health practitioner.
b) Establish if inferential data collected from connected devices could potentially give an indication of the primary user’s emotional state.
A test environment was constructed where an Amazon Echo VCD with default configuration was linked to several accounts, applications and IoT devices through the standard settings. It should be noted that an Amazon account is a mandatory requirement for configuring the Amazon Echo. Table 1 lists the devices, accounts and applications that were linked to the VCD for testing purposes.
This section offers an excerpt from the query script and responses in Table 2. In addition, a summary of the information gained is included. Each example lists the question that would be posed by a healthcare professional, the equivalent question posed to Alexa to return the desired information and also the VCD’s response. Informati on gained directly from the Alexa application is also shown in Table 2.
The Alexa application documents a history of all interactions with the Echo device. The application also documents a list of all the music selections played via Amazon music. When linked with a wearable fitness tracker such as Fitbit, the device al so relays information relating to the sleep patterns and activity/energy levels of the primary user. Although subtle, the combined inferential information gathered from these devices has the potential to indicate the emotional state of the user. This may be achieved though application of natural language processing algorithms to extract the key words that relate to emotion. Through these mechanisms there is the facility for the parent company, in this case Amazon, to harvest, analyses and potentially profit from the data collected.
This paper illustrates the ease by which indicators of emotions such as sadness, and indeed the associated condition of depression, may be elicited from an individual Amazon Echo device. While the probable value of this observation in terms of improving health and providing emotional support are clear, for parent companies such as Amazon, more lucrative applications of the data exist. This is inclusive of more effective and accurately targeted advertisements and recommendations. Ethically this is questionable, as the implications of this are significant when based on patterns detected relating to user emotion and potential mental health.
Amazon is primarily a retailer and the more effective the company is at marketing through target advertising, the more profitable they will become. Algorithms that indicate whether an individual may be suffering from depression could potentially lead to an unethical advertisement for medication, exercise programmes, dietary advice and counselling services. While these targeted advertisements may be helpful to the individual, companies like Amazon will increase revenue based on the unethically gathered inferential data relating to consumers. 5
Although emotional recognition and digital empathy are idealistic goals wi thin the development of AI and intelligent agents, the current technology available lacks outward emotional understanding and the personas contribute only alexithymic responses. Despite extensive research by large multinational technological organizations, authentic human-like empathic interactions with intelligent agents have not yet been achieved. Consequently, users are lulled into a false sense of security where they believe that their emotions remain private.
This paper has demonstrated that despite Alexa’s demonstrated lack of emotion and emotional understanding, VCDs such as the Amazon Echo have the ability to deduce emotions such as sadness through inferential data. This is displayed through responses to questions that offer the same information as those posed by health practitioners to establish potential cases of depression. This type of data paves the way for parent companies to effectively target future advertising and build EMOTOgraphic models. As users are presented with no indication of this by intelligent agents, most would be unaware that combined inferential data could be so revealing and potentially extremely profitable from a sales and marketing perspective.
As VCDs incorporate improved implementations of affective computing, the ability to build user trust as well as communicate an understanding of emotions will only increase. This potentially leads to great ethical and privacy concerns as intelligent agents such as Alexa are gradually and incrementally cured of Alexithymia indicators.